[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/10-best-practices-for-using-ai-powered-gitlab-duo-chat":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Michael Friedrich":701,"blog-related-posts-en-us-10-best-practices-for-using-ai-powered-gitlab-duo-chat":715,"next-steps-en-us":756,"blog-promotions-en-us":766},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":12,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/10-best-practices-for-using-ai-powered-gitlab-duo-chat.yml","10 Best Practices For Using Ai Powered Gitlab Duo Chat",[7],"michael-friedrich",null,"ai-ml",{"slug":11,"featured":12,"template":13},"10-best-practices-for-using-ai-powered-gitlab-duo-chat",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"10 best practices for using AI-powered GitLab Duo Chat","Explore tips and tricks for integrating GitLab Duo Chat into your AI-powered DevSecOps workflows. Plus, expert advice on how to refine chat prompts for the best results.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097639/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%281%29_77JeTV9gAmbXM0224acirV_1750097638765.png","2024-04-02","Getting into a conversation with AI can be challenging. What question do you start with? How do you frame the question? How much context is needed? Will the conversation provide the best and most efficient results?\n\nIn this tutorial, we explore 10 tips and best practices to integrate GitLab Duo Chat into your AI-powered DevSecOps workflows and refine your prompts for the best results.\n\n[Get started: Keep GitLab Duo Chat open and in sight](#get-started-keep-gitlab-duo-chat-open-and-in-sight)\n\n[10 best practices for using GitLab Duo Chat](#10-best-practices-for-using-gitlab-duo-chat)\n\n1. [Have a conversation](#1.-have-a-conversation)\n2. [Refine the prompt for more efficiency](#2.-refine-the-prompt-for-more-efficiency)\n3. [Follow prompt patterns](#3.-follow-prompt-patterns)\n4. [Use low-context communication](#4.-use-low-context-communication)\n5. [Repeat yourself](#5.-repeat-yourself)\n6. [Be patient](#6.-be-patient)\n7. [Reset and start anew](#7.-reset-and-start-anew)\n8. [Gain efficiency with slash commands in the IDE](#8.-gain-efficiency-with-slash-commands-in-the-ide)\n9. [Refine the prompt for slash commands](#9.-refine-the-prompt-for-slash-commands)\n10. [Get creative with slash commands](#10.-get-creative-with-slash-commands)\n\nBonus content:\n- [Shortcuts](#shortcuts)\n- [Fun exercises](#fun-exercises)\n- [Learn more](#learn-more)\n\n> Live demo! Discover the future of AI-driven software development with our GitLab 17 virtual launch event. [Register today!](https://about.gitlab.com/eighteen/)\n\n## Get started: Keep GitLab Duo Chat open and in sight\n\n[GitLab Duo Chat](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html) is available in the GitLab UI, Web IDE, and supported programming IDEs, for example, VS Code.\n\nIn VS Code, you can open GitLab Duo Chat in the default left pane. You can also drag and drop the icon into the right pane. This allows you to keep Chat open while you write code and navigate the file tree, perform Git actions, etc. To reset the Chat location, open the command palette (by pressing the `Command+Shift+P` (on macOS) or `Ctrl+Shift+P` (on Windows/Linux) keyboard shortcut and then type `View: Reset View Locations`. The following short video shows you how to do it.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/foZpUvWPRJQ\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nThe Web IDE and VS Code share the same framework – the same method works in the Web IDE for more efficient workflows.\n\n![Chat in Web IDE](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097645344.png)\n\n## 10 best practices for using GitLab Duo Chat\n\n### 1. Have a conversation\n\nChats are conversations, not search forms.\n\nFor the first conversation icebreaker, you can start with the same search terms similar to a browser search and experiment with the response and output. In this example, let's start with a C# project and best practices.\n\n> c# start project best practices\n\n![Chat prompt for C# start project best practices and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097646/Blog/Content%20Images/Blog/Content%20Images/image11_aHR0cHM6_1750097645345.png)\n\nThe response is helpful to understand a broad scope of C#, but does not kickstart immediate best practices. Let's follow up with a more focused question in the same context.\n\n> Please show the project structure for the C# project.\n\n![Chat prompt for project structure for the C# project and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image9_aHR0cHM6_1750097645346.png)\n\nThis answer is helpful. Next, let's follow up with a Git question, and use the same question structure: Direct request to show something.\n\n> Show an example for a .gitignore for C#\n\n![Chat prompt for a .gitignore for C# and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image25_aHR0cHM6_1750097645347.png)\n\nContinue with CI/CD and ask how to build the C# project.\n\n> Show a GitLab CI/CD configuration for building the C# project\n\n![Chat prompt for GitLab CI/CD configuration for building C# project and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image16_aHR0cHM6_1750097645349.png)\n\nIn this example, Chat encouraged us to request specific changes. Let's ask to use the .NET SDK 8.0 instead of 6.0.\n\n> In the above example, please use the .NET SDK 8.0 image\n\n![Chat prompt to use .NET SDK 8.0 image and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image32_aHR0cHM6_1750097645350.png)\n\nThe CI/CD configuration uses the .NET command line interface (CLI). Maybe we can use that for more efficient commands to create the projects and tests structure, too?\n\n> Explain how to create projects and test structure on the CLI\n\n![Chat prompt to explain how to create projects and test structure on the CLI and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image14_aHR0cHM6_1750097645351.png)\n\nOf course, we could execute these commands in the terminal, but what if we wanted to stay in VS Code? Let's ask Chat.\n\n> Explain how to open a new terminal in VS Code\n\n![Chat prompt to explain how to open a new terminal in VS Code and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750097645351.png)\n\n### 2. Refine the prompt for more efficiency\n\nThink of GitLab Duo Chat as a human, and engage with full sentences that provide as much context into your thoughts and questions.\n\nExperienced browser search users might know this approach to queries: Build up the question, add more terms to refine the scope, and restart the search after opening plenty of tabs.\n\nIn a browser search, this probably would result in four to five different search windows.\n\n```markdown\nc# start project best practices\nc# .gitignore\nc# gitlab cicd\nc# gitlab security scanning\nc# solutions and projects, application and tests\n```\n\nYou can follow this strategy in a chat conversation, too. It requires adding more context, making it a conversational approach. GitLab Duo Chat enables you to ask multiple questions in one conversation request. Example: You need to start with a new C# project, apply best practices, add a `.gitignore` file, and configure CI/CD and security scanning, just like in the above search. In Chat, you can combine the questions into one request.\n\n> How can I get started creating an empty C# console application in VS Code? Please show a .gitignore and .gitlab-ci.yml configuration with steps for C#, and add security scanning for GitLab. Explain how solutions and projects in C# work, and how to add a test project on the CLI.\n\n![Chat prompt adding more context and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image37_aHR0cHM6_1750097645352.png)\n\nIn this response, Chat suggests to ask for specific configuration examples in follow-up questions in the conversation. Async practice: Create follow-up questions. You can omit `C#` as context in the same chat session.\n\n> Please show an example for a .gitignore. Please show a CI/CD configuration. Include the SAST template.\n\n### 3. Follow prompt patterns\n\nFollow the pattern: `Problem statement, ask for help, provide additional requests`. Not everything comes to mind when asking the first question – don't feel blocked, and instead start with `Problem statement, ask for help` in the first iteration.\n\n> I need to fulfill compliance requirements. How can I get started with Codeowners and approval rules?\n\n![Chat prompt to get started with Codeowners and approval rules and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image19_aHR0cHM6_1750097645352.png)\n\nThe answer is helpful but obviously generic. Now, you may want to get specific help for your team setup.\n\n> Please show an example for Codeowners with different teams: backend, frontend, release managers.\n\n![Chat prompt to show an example for Codeowners with different teams: backend, frontend, release managers and reponse ](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image31_aHR0cHM6_1750097645353.png)\n\nAn alternative is to describe the situation you are in and to ask for input. It can feel a bit like a conversation to follow the STAR model (Situation, Task, Action, Results).\n\n> I have a Kubernetes cluster integrated in GitLab. Please generate a Yaml configuration for a Kubernetes service deployment. Explain how GitOps works as a second step. How to verify the results?\n\n![Chat prompt with multiple questions and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image27_aHR0cHM6_1750097645354.png)\n\n### 4. Use low-context communication\n\nProvide as much context as needed to provide an answer. Sometimes, the previous history or opened source code does not provide that helpful context. To make questions more efficient, apply a pattern of [low-context communication](https://handbook.gitlab.com/handbook/company/culture/all-remote/effective-communication/#understanding-low-context-communication), which is used in all-remote communication at GitLab.\n\nThe following question did not provide enough context in a C++ project.\n\n> Should I use virtual override instead of just override?\n\n![Chat prompt asking if the users should use virtual override instead of just override and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image34_aHR0cHM6_1750097645354.png)\n\nInstead, try to add more context:\n\n> When implementing a pure virtual function in an inherited class, should I use virtual function override, or just function override? Context is C++.\n\n![Chat prompt with more detail and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image36_aHR0cHM6_1750097645355.png)\n\nThe example is also shown in the [GitLab Duo Coffee Chat: Refactor C++ functions into OOP classes for abstract database handling](https://youtu.be/Z9EJh0J9358?t=2190).\n\n### 5. Repeat yourself\n\nAI is not predictable. Sometimes, it may not answer with the expected results, or does not produce source code examples or configuration snippets because it lacked context. It is recommended to repeat the question and refine the requirements.\n\nIn the following example, we want to create a C# application. In the first attempt, we did not specify the application type – C# can be used to create console/terminal but also UI applications. The result also does not provide an empty example source code. The second, repeated prompt adds two more words - `console` and `empty`.\n\n> How can I get started creating an C# application in VSCode?\n>\n> How can I get started creating an empty C# console application in VSCode?\n\nThe results in the prompt differ. The first response is helpful to get started by following the instructions in the VS Code window, but it does not tell us where the source code is located and how to modify it. The repeated prompt with refinements modifies the response and provides instructions how to override the default template with some “hello world” code.\n\n![Chat prompt with repeated prompt with modifications and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image28_aHR0cHM6_1750097645355.png)\n\nYou can also combine repeat and refine strategies, and ask Chat to show an example for application code and tests.\n\n> How can I get started creating an empty C# console application in VSCode? Please show an example for application and tests.\n\n![Chat prompt that asks for example for application and tests and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097645356.png)\n\n#### Repeat yourself after generic questions\n\nWhen asking generic technology questions, GitLab Duo Chat might not be able to help. In the following scenario, I wanted to get a suggestion for Java build tools and framework, and it did not work. There could be many answers: Maven, Gradle, etc., as build tools, and [100+ Java frameworks](https://en.wikipedia.org/wiki/List_of_Java_frameworks), depending on the technology stack and requirements.\n\n![Chat prompt for Java build tools and framework and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097645356.png)\n\nLet's assume that we want to focus on a customer environment with [Java Spring Boot](https://spring.io/projects/spring-boot).\n\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example.\n\n![Chat prompt that asks for more, including a hello world example and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image26_aHR0cHM6_1750097645357.png)\n\nThis provides great results already. As an async exercise, repeat the prompt, and ask how to deploy the application, adding more refinements in each step. Alternatively, you can make it a follow-up conversation.\n\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example. Show how to build and deploy the application in CI/CD.\n>\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example. Show how to build and deploy the application in CI/CD, using container images.\n>\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example. Show how to build and deploy the application in CI/CD, using container images. Use Kubernetes and GitOps in GitLab.\n\n### 6. Be patient\n\nSingle words or short sentences might not generate the desired results, [as shown in this video example](https://youtu.be/JketELxLNEw?t=1220). Sometimes, GitLab Duo Chat is able to guess from available data, but sometimes also might insist on providing more context.\n\nExample: `labels` matches the GitLab documentation content.\n\n![Chat prompt about labels and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image12_aHR0cHM6_1750097645357.png)\n\nRefine the question to problem statements and more refinements for issue board usage.\n\n> Explain labels in GitLab. Provide an example for efficient usage with issue boards.\n\n![Chat prompt that includes asking for an example and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image21_aHR0cHM6_1750097645358.png)\n\nOr use a problem statement, followed by a question and the ask for additional examples.\n\n> I don't know how to use labels in GitLab. Please provide examples, and how to use them for filters in different views. Explain these views with examples.\n\n![Chat prompt with problem statement and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image10_aHR0cHM6_1750097645358.png)\n\nAlso, avoid `yes/no` questions and instead add specific context.\n\n> Can you help me fix performance regressions?\n\n![Chat promptt that asks for help with fixing performance regressions and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image18_aHR0cHM6_1750097645359.png)\n\nInstead, provide the context of the performance regression, including the programming languages, frameworks, technology stack, and environments. The following example uses an environment from some years ago, which can still be accurate today.\n\n> My PHP application encounters performance regressions using PHP 5.6 and MySQL 5.5. Please explain potential root causes, and how to address them. The app is deployed on Linux VMs.\n\n![Chat prompt that includes more detail and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image24_aHR0cHM6_1750097645360.png)\n\n### 7. Reset and start anew\n\nSometimes, the chat history shows a different learning curve and provides the wrong context for follow-up questions. Or, you asked specific questions where GitLab Duo Chat cannot provide answers. Since generative AI is not predictable, it might also lack the ability to provide certain examples, but think it gave them in a future response (observed in Chat Beta). The underlying large language models, or LLMs, sometimes might insist on giving a specific response, in an endless loop.\n\n> How can I get started creating an empty C# console application in VSCode? Please show a .gitignore and .gitlab-ci.yml configuration with steps for C#, and add security scanning for GitLab. Explain how solutions and projects in C# work, and how to add a test project on the CLI.\n\nAfter asking the question above with an example configuration, I wanted to reduce the scope of the question to get a more tailored response. It did not work as expected, since Chat knows about the chat history in context, and refers to previous answers.\n\n> How can I get started creating an empty C# console application in VSCode? Please show a .gitignore and .gitlab-ci.yml configuration with steps for C#.\n\n![Chat prompt that asks for configuration examples and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image23_aHR0cHM6_1750097645360.png)\n\nTo force Chat into a new context, use `/reset` as slash command to reset the session, and repeat the question to get better results. You can also use `/clean` or `/clear` to delete all messages in the conversation.\n\n### 8. Gain efficiency with slash commands in the IDE\n\n#### Explain code\n\n- Q: Generated code? Existing code? Legacy code?\n- A: Use the [`/explain` slash command in the IDE](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html#explain-code-in-the-ide).\n- A2: Refine the prompt with more focused responses, for example: `/explain focus on potential shortcomings or bugs`.\n\n![Chat prompt with /explain slash command](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/gitlab_duo_chat_slash_commands_explain_01_aHR0cHM6_1750097645361.png)\n\n![Chat prompt with refined prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image6_aHR0cHM6_1750097645361.png)\n\n#### Refactor code\n\n- Q: Unreadable code? Long spaghetti code? Zero test coverage?\n- A: Use the [`/refactor` slash command in the IDE](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html#refactor-code-in-the-ide).\n- A2: Refine the prompt for more targeted actions, for example object-oriented patterns: `/refactor into object-oriented classes with methods and attributes`.\n\n![Chat prompt with /refactor slash command](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image35_aHR0cHM6_1750097645362.png)\n\n![Chat prompt with refined prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image30_aHR0cHM6_1750097645362.png)\n\n#### Generate tests\n\n- Q: Testable code but writing tests takes too much time?\n- A: Use the [`/tests` slash command in the IDE](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html#write-tests-in-the-ide).\n- A2: Refine the prompt for specific test frameworks, or test targets. You can also instruct the prompt to focus on refactoring, and then generate tests: `/tests focus on refactoring the code into functions, and generate tests`.\n\n![Chat prompt with /tests slash command](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image29_aHR0cHM6_1750097645363.png)\n\n![Chat prompt with refined prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750097645363.png)\n\nMore practical examples in complete development workflows are available in the [GitLab Duo examples](https://docs.gitlab.com/ee/user/gitlab_duo_examples.html) documentation.\n\n### 9. Refine the prompt for slash commands\n\nYou will see refined prompts tips in this blog post a lot. It is one of the ingredients for better AI-powered workflow efficiency. Slash commands are no different, and allow for better results in GitLab Duo Chat.\n\nA customer recently asked: \"Can code explanations using `/explain` create comments in code?\" The answer is: no. But you can use the Chat prompt to ask follow-up questions, and ask for a summary in a code comment format. It requires the context of the language.\n\nThe following example with a [C++ HTTP client code using the curl library](https://gitlab.com/gitlab-da/use-cases/ai/ai-workflows/gitlab-duo-prompts/-/blob/5cc9bdd65ee8ee16c548bea0402c18f8209d4d06/chat/slash-commands/c++/cli.cpp) needs more documentation. You can refine the `/explain` prompt by giving more refined instructions to explain the code by adding code comments, and then copy-paste that into the editor.\n\n> /explain add documentation, rewrite the code snippet\n\n![Chat prompt to add documentation and rewrite code snippet and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image13_aHR0cHM6_1750097645363.png)\n\nAlternatively, you can ask Chat to `/refactor` the source code, and generate missing code comments through a refined prompt.\n\n> /refactor add code comments and documentation\n\n![Chat prompt to refactor source code and generate code comments](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image15_aHR0cHM6_1750097645364.png)\n\n### 10. Get creative with slash commands\n\nWhen the Chat prompt does not know an answer to a question about the source code or programming language, look into the slash commands `/explain`, `/refactor`, and `/tests` and how much they can help in the context.\n\nIn the following example, an SQL query string in C++ is created in a single line. To increase readability, and also add more database columns in the future, it can be helpful to change the formatting into a multi-line string.\n\n> std::string sql = \"CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT NOT NULL, email TEXT NOT NULL)\";\n\nYou can ask GitLab Duo Chat about it, for example, with the following question:\n\n> How to create a string in C++ using multiple lines?\n\nChat may answer with an explanation and optional, a source code example. In this context, it can interpret the question to create a C++ string value with multiple lines, for example, using the `\\n` character, assigned to a variable.\n\nThe requirement instead is to only format the written code, and variable value assignment in multiple lines. The string value itself does not need to contain a multi-line string representation.\n\nThere is an alternative for additional context in VS Code and the Web IDE: Select the source code in question, right-click, and navigate into `GitLab Duo Chat > Refactor`. This opens the Chat prompt and fires the `/refactor` code task immediately.\n\nAlthough, the code task might not bring the expected results. Refactoring a single-line SQL string can mean a lot of things: Use multiple lines for readability, create constants, etc.\n\nCode tasks provide an option to refine the prompt. You can add more text after the `/refactor` command, and instruct GitLab Duo Chat to use a specific code type, algorithm, or design pattern.\n\nLet's try it again: Select the source code, change focus into Chat, and type the following prompt, followed by `Enter`.\n\n> /refactor into a multi-line written string. Show different approaches for all C++ standards.\n\n![Chat prompt to refactor into a multi-line written string and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image17_aHR0cHM6_1750097645364.png)\n\n**Tip:** You can use GitLab Duo Code Suggestions to refine the source code even more after refactoring, or use alternative `/refactor` prompt refinements.\n\n>/refactor into a multi-line written string, show different approaches\n>\n> /refactor into multi-line string, not using raw string literals\n>\n> /refactor into a multi-line written string. Make the table name parametrizable\n\nAn alternative approach with the `stringstream` type is shown in the [GitLab Duo Coffee Chat: Refactor C++ functions into OOP classes for abstract database handling](https://www.youtube.com/watch?v=Z9EJh0J9358), [MR diff](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-coffee-chat/gitlab-duo-coffee-chat-2024-01-23/-/commit/7ea233138aed46d77e6ce0d930dd8e10560134eb#4ce01e4c84d4b62df8eed159c2db3768ad4ef8bf_33_35).\n\n#### Explain vulnerabilities\n\nIt might not always work, but the `/explain` slash command can be asked about security vulnerability explanations, too. In this example, the [C code](https://gitlab.com/gitlab-da/use-cases/ai/ai-workflows/gitlab-duo-prompts/-/blob/5a5f293dfbfac7222ca4013d8f9ce9b462e4cd3a/chat/slash-commands/c/vuln.c) contains multiple vulnerabilities for strcpy() buffer overflows, world writable file permissions, race condition attacks, and more.\n\n> /explain why this code has multiple vulnerabilities\n\n![Chat prompt about the code's multiple vulnerabilities](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image20_aHR0cHM6_1750097645365.png)\n\n#### Refactor C code into Rust\n\nRust provides memory safety. You can ask Duo Chat to refactor the vulnerable [C code](https://gitlab.com/gitlab-da/use-cases/ai/ai-workflows/gitlab-duo-prompts/-/blob/5a5f293dfbfac7222ca4013d8f9ce9b462e4cd3a/chat/slash-commands/c/vuln.c) into Rust, using `/refactor into Rust`. Practice with more refined prompts to get better results.\n\n> /refactor into Rust and use high level libraries\n\n![Chat prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image8_aHR0cHM6_1750097645366.png)\n\n### Shortcuts\n\nGive these shortcuts a try in your environment, and practice async using GitLab Duo Chat.\n\n1. Inspect vulnerable code from CVEs, and ask what it does, and how to fix it, using `/explain why is this code vulnerable`.\n**Tip:** Import open-source projects in GitLab to take advantage of GitLab Duo Chat code explanations.\n1. Try to refactor code into new programming languages to help legacy code migration plans.\n1. You can also try to refactor Jenkins configuration into GitLab CI/CD, using `/refactor into GitLab CI/CD configuration`.\n\n### Fun exercises\n\nTry to convince Chat to behave like Clippy.\n![Chat prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image22_aHR0cHM6_1750097645366.png)\n\nAsk about GitLab's mission: \"Everyone can contribute.\"\n\n![Chat prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image33_aHR0cHM6_1750097645367.png)\n\n### Learn more\n\nThere are many different environments and challenges out there. 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statement",{"items":691},[692,695,698],{"text":693,"config":694},"Terms",{"href":520,"dataGaName":521,"dataGaLocation":468},{"text":696,"config":697},"Cookies",{"dataGaName":530,"dataGaLocation":468,"id":531,"isOneTrustButton":12},{"text":699,"config":700},"Privacy",{"href":525,"dataGaName":526,"dataGaLocation":468},[702],{"id":703,"title":18,"body":8,"config":704,"content":706,"description":8,"extension":27,"meta":710,"navigation":12,"path":711,"seo":712,"stem":713,"__hash__":714},"blogAuthors/en-us/blog/authors/michael-friedrich.yml",{"template":705},"BlogAuthor",{"name":18,"config":707},{"headshot":708,"ctfId":709},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659879/Blog/Author%20Headshots/dnsmichi-headshot.jpg","dnsmichi",{},"/en-us/blog/authors/michael-friedrich",{},"en-us/blog/authors/michael-friedrich","lJ-nfRIhdG49Arfrxdn1Vv4UppwD51BB13S3HwIswt4",[716,729,744],{"content":717,"config":727},{"title":718,"description":719,"authors":720,"heroImage":722,"date":723,"body":724,"category":9,"tags":725},"GitHub Copilot's new policy for AI training is a governance wake-up call","Learn what GitHub's Copilot policy change means for regulated industries, and why GitLab's commitment to customer data privacy matters.",[721],"Allie Holland","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776347152/unw3mzatkd5xyfbzcnni.png","2026-04-20","GitHub recently [announced](https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/) a significant change to how it handles data from Copilot users. Starting April 24, 2026, interaction data from Copilot Free, Pro, and Pro+ users, including inputs, outputs, code snippets, and associated context, will be used to train AI models by default, unless users actively opt out. Copilot Business and Enterprise customers are exempt under existing contract terms.\n\nFor organizations in regulated industries, including finance, healthcare, defense, and public sector, the policy shift raises questions that go beyond individual developer preferences. It forces a harder look at a question that engineering and security leaders should be asking every AI vendor in their stack: Do you train on our code? \n\nGitLab's answer is no. GitLab does not train AI models on customer code at any tier, and AI vendors are contractually prohibited from using customer inputs or outputs for their own purposes. The [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) makes that commitment auditable: a single location documenting which models power which features, how data is handled, subprocessor relationships, and data retention periods. The GitLab AI Transparency Center also lists the compliance status of each feature, including confirmation that GitLab's current AI features do not qualify as high-risk systems under the EU AI Act. It's a standard GitLab CEO Bill Staples has consistently [reiterated](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf?utm_source=share&utm_medium=member_desktop&rcm=ACoAABsu7EUBcb_a1-JHKS9RC0B5rf8Ye-5XM60) and one reflected in GitLab's mission and [Trust Center](https://trust.gitlab.com/).\n\n## What the policy change actually means\n\nGitHub's announcement also specifies that the data may be shared with GitHub affiliates, including Microsoft, for AI development purposes.\n\nA policy change of this nature forces organizations to re-examine their AI governance posture, audit their Copilot license tiers, and confirm that the right controls are configured across their teams.\n\n## Why AI governance matters in regulated environments\n\nSource code is often among an organization's most sensitive intellectual property. It may contain references to internal systems, reflect proprietary business logic, or touch data flows governed by strict retention and access policies. When that code passes through an AI assistant, questions about training data usage, model vendor relationships, and data residency become compliance concerns.\n\nThe exposure is particularly acute for financial services firms that have invested in proprietary algorithms, fraud detection logic, credit risk models, underwriting rules, trading strategies. When AI tooling processes that code and uses it to train models serving competitors, vendor data practices become an IP concern.\n\nFinancial institutions operating under [the Federal Reserve's Supervisory Guidance on Model Risk Management (SR 11-7) and the](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) [Digital Operational Resilience Act (DORA)](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) are required to maintain documented, auditable oversight of third-party technology providers, including understanding how those providers handle data. Third-party AI tools used in development workflows increasingly fall within the scope of model risk oversight, and material changes to vendor data practices require updated documentation. \n\nIn the public sector, [the National Institute of Standards and Technology Special Publication 800-53 (NIST 800-53)](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and the [Federal Information Security Modernization Act (FISMA)](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) establish that sensitive or classified code must never leave a controlled boundary. For U.S. Department of Defense and intelligence community environments in particular, a vendor's default data posture is an operational concern. In healthcare, [the Health Insurance Portability and Accountability Act (HIPAA)](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties, and development environments that touch clinical systems increasingly fall within that scope.\n\nAcross all of these contexts, the common thread is the same: A vendor policy that changes data usage defaults, requires individual opt-out, and offers different protections depending on account tier introduces exactly the kind of uncontrolled variable that compliance teams cannot afford.\n\n## What regulated industries actually need from AI vendors\n\nRegulated organizations have largely moved past debating whether to adopt AI in development workflows. The focus now is on doing so in a way they can defend to regulators, boards, and customers. That shift has surfaced a consistent set of requirements regardless of sector.\n\n**Contractual certainty.** Regulated firms need to know, with specificity, what happens to their data. A clear, documented, unconditional commitment is what's required, not something that varies by plan or requires action before a deadline.\n\n**Auditability.** Model risk management frameworks require organizations to understand and validate the AI systems they deploy, including the training data behind those models and the third parties involved in their development. Vendors who cannot answer these questions create documentation risk for the organizations relying on them.\n\n**Separation from vendor incentives.** When an AI vendor trains models on customer usage data, code and workflows become inputs to a system that also serves competitors. For institutions with proprietary trading logic, underwriting models, or fraud detection systems, that's a genuine IP exposure.\n\n## GitLab's position on AI data governance\n\nGitLab does not use customer code to train AI models. This commitment applies at every tier, and AI vendors are contractually prohibited from using inputs or outputs associated with GitLab customers for their own purposes.\n\nThis is a deliberate architectural and policy choice, not a feature of a particular pricing tier. As GitLab's [post on enterprise independence](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) notes, data governance has become \"an increasingly critical factor in enterprise technology decisions, driven by a complex web of national and regional data protection laws and growing concern about control over sensitive intellectual property.\"\n\nGitLab is also cloud-neutral and model-neutral while supporting self-hosted deployments, not commercially tied to any single cloud provider or large language model (LLM). That i[ndependence matters](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) for regulated organizations evaluating vendor concentration risk. The [AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including material changes to how AI vendors treat customer data, a direct response to the governance requirements under frameworks like [DORA](https://handbook.gitlab.com/handbook/legal/dora/). \n\n## The governance gap AI teams need to close\n\nGitHub's policy update is a reminder that for organizations in regulated industries, understanding exactly how an AI tool handles data is a prerequisite for using it at all. That means asking vendors for clear, documented answers: Is our data used for model training? Who are your AI model subprocessors? What happens if a vendor changes its data practices? Can we deploy in a way that keeps all AI processing within our own infrastructure? What indemnification do you offer for AI-generated output?\n\nVendors who can answer those questions clearly, and document those answers in an auditable form, are vendors you can build on. **Those who cannot will create compliance debt every time they ship a policy update.** And when a vendor can change its data practices with 30 days notice, that's not a partnership built for regulated industries. That's a liability.\n\n> Learn more about GitLab's approach to AI governance at the [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/).",[23,726],"product",{"featured":31,"template":13,"slug":728},"github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call",{"content":730,"config":742},{"title":731,"description":732,"authors":733,"body":736,"heroImage":737,"date":738,"category":9,"tags":739},"GitLab and Vertex AI on Google Cloud: Advancing agentic software development","Learn how Google Cloud customers are standardizing on GitLab and Vertex AI for foundation models, enterprise controls, and Model Garden breadth.\n",[734,735],"Regnard Raquedan","Rajesh Agadi","GitLab Duo Agent Platform is helping redefine how organizations build, secure, and deliver software. Since its general availability in January 2026, the platform is bringing agentic AI to every phase of the software development lifecycle. Duo Agent Platform is an intelligent orchestration layer where software teams, and their specialized agents plan, code, review, and remediate security vulnerabilities together.\n\nThrough this exciting partnership, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) automates software development orchestration and lifecycle context via its integration with Vertex AI on Google Cloud, which powers the model tier for agent calls. Software teams keep working on issues, merge requests, pipelines, and security workflows while inference follows the Google Cloud posture they already defined. \n\nAdvances in Google Cloud’s Vertex AI models expand how Google Cloud customers can use GitLab Duo Agent Platform in their environment. Customers get an AI-powered DevSecOps control plane in GitLab, backed by a rapidly advancing AI infrastructure foundation in Vertex AI and Duo Agent Platform’s flexible deployment and integration options. The combination enables more capable, governed agentic workflows that operate at enterprise scale.\n\n![Conceptual illustration of the GitLab Duo Agent Platform integrated with Google Cloud's Vertex AI to power agentic software development and governed AI workflows](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776165990/b7jlux9kydafncwy8spc.png)\n\n## Agents that work across the full lifecycle\n\nMany AI tools focus on a single task: generating code faster. GitLab Duo Agent Platform goes further. It orchestrates AI agents across the entire software development lifecycle (SDLC), from planning through security review to delivery, across many teams with many projects and releases. At this scale, AI coding assistants are necessary for continuous innovation but not sufficient. \n\nSingle-purpose coding assistants rarely see the full state of a project. Backlog shape, open merge requests, failing jobs, and security findings live in GitLab, but a separate chat window in a coding assistant does not inherit that full picture of the SDLC. The gap shows up as manual handoffs, duplicate explanations to an AI that lacks context, and governance teams trying to map data flows across tools that were never designed as one system.\n\nGitLab Duo Agent Platform helps close that gap by running agents and flows on the same objects engineers use every day. Vertex AI then supplies the models and services those agents call when Google Cloud is your chosen inference home, with GitLab’s AI Gateway mediating access so administrators keep a clear map of what connects to what. For instance, GitLab Duo Planner Agent analyzes backlogs, breaks epics into structured tasks, and applies prioritization frameworks to help teams decide what to build next. Security Analyst Agent triages vulnerabilities, details risks in plain language, and recommends remediation in priority order. Built-in flows connect these agents into end-to-end processes, without requiring developers to manage every handoff manually.\n\nAgentic Chat in GitLab Duo Agent Platform ties the experience together for developers. They query in natural language to get context-aware responses with multi-step reasoning that draws on the full state of a project: its issues, merge requests, pipelines, security findings, and codebase. Because GitLab serves as the system of record for the SDLC with a unified data model, GitLab Duo agents operate with lifecycle context that falls outside the reach of standalone, tool-specific AI assistants.\n\n### Amplified by Vertex AI\n\nGitLab Duo Agent Platform is designed to be model-flexible, routing different capabilities to different models based on what performs best for a given task. That architectural choice pays off on Google Cloud, where Vertex AI acts as the managed environment for foundation models and related services, providing a broad model ecosystem and managed infrastructure that helps push the platform's capabilities further.\n\nThe latest generations of AI models available through Vertex AI bring significant improvements in reasoning, tool use, and long-context understanding compared to previous iterations — the same properties that GitLab's agents rely on across many projects and teams with large, complex codebases. Longer context windows and richer tool integration in the underlying models expand what agents can accomplish in a single pass, which is especially important for workloads like deep backlog analysis or monorepo security review.\n\n[Vertex AI Model Garden](https://cloud.google.com/model-garden), with access to a wide range of foundation models, gives customers the breadth to make these choices based on performance, cost, and regulatory requirements rather than vendor lock-in.\n\nMoreover, GitLab customers can use Bring Your Own Model (BYOM) for Duo Agent Platform so approved providers and gateways land where your security model expects them. GitLab’s [18.9 launch coverage of self-hosted Duo Agent Platform and BYOM](https://about.gitlab.com/blog/agentic-ai-enterprise-control-self-hosted-duo-agent-platform-and-byom/) describes how that wiring works. With this deployment option, customers gain access to a wider set of model options they can tailor to their software development process: the right model for the right workflow, with the right guardrails.\n\nFor GitLab, the decision to build on Vertex AI was driven by the need for enterprise-grade reliability and unparalleled model breadth. Vertex AI and Model Garden completely abstract the heavy lifting of LLM hosting — meaning rapid version delivery, robust security, and strict governance are seamlessly built into the integration. Beyond offering Gemini models, Vertex AI provides global, low-latency access to a vast catalog of third-party and open-source models. \n\nCombined with Google Cloud's industry-leading approach to data privacy and model protection, Vertex AI emerged as the clear choice to power GitLab's next-generation developer experience. \n\nBy integrating Vertex AI Model Garden into its backend, GitLab supercharges its DevSecOps platform without passing any complexity on to users. Development teams are not burdened with evaluating or managing underlying LLMs; instead, they experience a streamlined, AI-assisted workflow for building their applications. \n\nGitLab completely abstracts cloud orchestration, enabling developers to focus entirely on writing great code, while Vertex AI powers the features and functionality that assist them.\n\n## What this means for customers on Google Cloud\n\nGitLab Duo Agent Platform already delivers AI agents that operate across the full software lifecycle within a single, governed system of record. On Google Cloud, it enables rapid innovation as Vertex AI continues to advance the model and infrastructure layers. \n\nFor Google Cloud customers, this integration means streamlined software delivery while maintaining strict enterprise governance. For platform engineering groups, it means normalizing which Vertex-backed models power suggestions, analysis, and remediation inside GitLab instead of cataloging dozens of client-side tools. Security programs benefit when agents propose and validate fixes in the same place developers already triage findings, cutting context switching and reducing work that would otherwise spill into unmanaged channels.\n\nFrom a cloud economics and policy angle, drawing agent inference toward Vertex from within GitLab keeps usage nearer to the agreements and controls you already run on Google Cloud, which helps avoid duplicate spend and shadow paths that bypass procurement.\n\nBecause Vertex AI is an underlying infrastructure provider for GitLab Duo Agent Platform, organizations are enabled to dramatically lift developer productivity without the overhead and risk of managing fragmented AI toolchains. Teams stay aligned within a single, secure system of record, helping them build applications faster and ship with confidence.\n\nThe GitLab and Google Cloud collaboration has been building since 2018. Today, it represents one of the most comprehensive paths for organizations moving from AI experiments to fully governed, agentic software development on Google Cloud. As both platforms continue to advance — GitLab expanding its agent orchestration and developer context, and Vertex AI pushing the boundaries of model capability and agent infrastructure — the value for joint customers will continue to grow.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/free-trial/) to experience the power of GitLab and Vertex AI on Google Cloud.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663121/Blog/Hero%20Images/LogoLockupPlusLight.png","2026-04-14",[23,277,740,741,726],"google","news",{"featured":12,"template":13,"slug":743},"gitlab-and-vertex-ai-on-google-cloud",{"content":745,"config":754},{"heroImage":746,"title":747,"description":748,"authors":749,"date":751,"category":9,"tags":752,"body":753},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643639/sapu29gmlgtwvhggmj6k.png","Extend GitLab Duo Agent Platform: Connect any tool with MCP","Learn how to connect external tools to GitLab Duo Agent Platform using MCP. Step-by-step setup with three practical workflow demos.",[750],"Albert Rabassa","2026-03-05",[9,726,24],"Managing software development often means juggling multiple tools: tracking issues in Jira, writing code in your IDE, and collaborating through GitLab. Context switching between these platforms disrupts focus and slows down delivery.\n\nWith GitLab Duo Agent Platform's [MCP](https://about.gitlab.com/topics/ai/model-context-protocol/) support, you can now connect Jira or any tool that supports MCP directly to your AI-powered development environment. Query issues, update tickets, and sync your workflow — all through natural language, without ever leaving your IDE.\n\n## What you'll learn\n\nIn this tutorial, we'll walk you through:\n\n* **Setting up the Jira/Atlassian OAuth application** for secure authentication\n* **Configuring GitLab Duo Agent Platform** as an MCP client\n* **Three practical use cases** demonstrating real-world workflows\n\n## Prerequisites\n\nBefore getting started, ensure you have the following:\n\n| Requirement | Details |\n| ---- | ----- |\n| **GitLab instance** | GitLab 18.8+ with Duo Agent Platform enabled |\n| **Jira account** | Jira Cloud instance with admin access to create OAuth applications |\n| **IDE** | Visual Studio Code with GitLab Workflow extension installed |\n| **MCP support** | MCP support enabled in GitLab |\n\n\n## Understanding the architecture\n\nGitLab Duo Agent Platform acts as an **MCP client**, connecting to the Atlassian MCP server to access your Jira project management data. Atlassian  MCP server handles authentication, translates natural language requests into API calls, and returns structured data back to GitLab Duo Agent Platform — all while maintaining security and audit controls.\n\n## Part 1: Configure Jira OAuth application\n\nTo securely connect GitLab Duo Agent Platform to your Jira instance, you'll need to create an OAuth 2.0 application in the Atlassian Developer Console. This grants to GitLab the MCP server authorized access to your Jira data.\n\n### Setup steps\n\nIf you prefer to configure manually, follow these steps:\n\n1. **Navigate to the Atlassian Developer Console**\n\n   * Go to [developer.atlassian.com/console/myapps](https://developer.atlassian.com/console/myapps)\n\n   * Sign in with your Atlassian account\n\n2. **Create a new OAuth 2.0 app**\n\n   * Click **Create** → **OAuth 2.0 integration**\n\n   * Enter a name (e.g., \"gitlab-dap-mcp\")\n\n   * Accept the terms and click **Create**\n\n3. **Configure permissions**\n\n   * Navigate to **Permissions** in the left sidebar.\n\n   * Add **Jira API** and configure the following scopes:\n\n     * `read:jira-work` — Read issues, projects, and boards\n\n     * `write:jira-work` — Create and update issues\n\n     * `read:jira-user` — Read user information\n\n4. **Set up authorization**\n\n   * Go to **Authorization** in the left sidebar\n\n   * Add a callback URL for your environment (`https://gitlab.com/oauth/callback`)\n\n   * Save your changes\n\n5. **Retrieve credentials**\n\n   * Navigate to **Settings**\n\n   * Copy your **Client ID** and **Client Secret**\n\n   * Store these securely — you'll need them for the MCP configuration\n\n\n### Interactive walkthrough: Jira OAuth setup\n\nClick on the image below to get started.\n\n\n[![Jira OAuth setup tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772644850/wnzfoq43nkkfmgdqldmr.png)](https://gitlab.navattic.com/jira-oauth-setup)\n\n\n## Part 2: Configure GitLab Duo Agent Platform MCP client\n\nWith your OAuth credentials ready, you can now configure GitLab Duo Agent Platform to connect to the Atlassian MCP server.\n\n### Create your MCP configuration file\n\nCreate the MCP configuration file in your GitLab project at `.gitlab/duo/mcp.json`:\n\n\n```json\n{\n  \"mcpServers\": {\n    \"atlassian\": {\n      \"type\": \"http\",\n      \"url\": \"https://mcp.atlassian.com/v1/mcp\",\n      \"auth\": {\n        \"type\": \"oauth2\",\n        \"clientId\": \"YOUR_CLIENT_ID\",\n        \"clientSecret\": \"YOUR_CLIENT_SECRET\",\n        \"authorizationUrl\": \"https://auth.atlassian.com/oauth/authorize\",\n        \"tokenUrl\": \"https://auth.atlassian.com/oauth/token\"\n      },\n      \"approvedTools\": true\n    }\n  }\n}\n```\n\nReplace `YOUR_CLIENT_ID` and `YOUR_CLIENT_SECRET` with the credentials you generated in Part 1.\n\n### Enable MCP in GitLab\n\n1. Navigate to your **Group Settings** → **GitLab Duo** → **Configuration**\n2. Make sure “Allow external MCP tools” is checked\n\n### Verify the connection\n\nOpen your project in VS Code and ask in GitLab Duo Agent Platform chat:\n\n```text\nWhat MCP tools do you have access to?\n```\n\nThen\n\n```text\nTest the MCP JIRA configuration in this project\n```\n\nAt this point you'll be redirected from the IDE to the MCP Atlassian website to approve access:\n\n![Redirect to MCP Atlassian website](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/z5acqjgguh0damnnde9g.png \"Redirect to MCP Atlassian website\")\n\n\u003Cbr>\u003C/br>\n\n![Approve access](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/rwowamm8nsubhpixtn3i.png \"Approve access\")\n\n\u003Cbr>\u003C/br>\n\n![Select your JIRA instance and approve](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/chuzqd0jeptfwvoj7wjr.png \"Select your JIRA instance and approve\")\n\n\u003Cbr>\u003C/br>\n\n![Success!](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/bsgti5iste2bzck19o5y.png \"Success!\")\n\n\u003Cbr>\u003C/br>\n\n### Verify with the MCP Dashboard\n\nGitLab also provides a built-in **MCP Dashboard** directly in your IDE for this.\n\nIn VS Code or VSCodium, open the Command Palette (`Cmd+Shift+P` on macOS, `Ctrl+Shift+P` on Windows/Linux) and search for **\"GitLab: Show MCP Dashboard\"**. The dashboard opens in a new editor tab and gives you:\n\n* **Connection status** for each configured MCP server\n* **Available tools** exposed by the server (e.g., `jira_get_issue`, `jira_create_issue`)\n* **Server logs** so you can see exactly which tools are being called in real time\n\n![MCP servers dashboard and status](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/mmvdfchucacsydivowvn.png \"MCP servers dashboard and status\")\n\n\u003Cbr>\u003C/br>\n\n![Server details and permissions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/tcocgdvovp2dl42pvfn8.png \"Server details and permissions\")\n\n\u003Cbr>\u003C/br>\n\n\n![MCP Server logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643466/mougvqqk1bozchaufsci.png \"MCP Server logs\")\n\n\u003Cbr>\u003C/br>\n\n### Interactive walkthrough: Testing MCP\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005495?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Testing MCP\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Part 3: Use cases in action\n\nNow that your integration is configured, let's explore three practical workflows that demonstrate the power of connecting Jira to GitLab Duo Agent Platform.\n\n### Planning assistant\n\n**Scenario:** You're preparing for sprint planning and need to quickly assess the backlog, understand priorities, and identify blockers.\n\nThis demo shows you how to:\n\n* Query the backlog\n* Identify unassigned high-priority issues\n* Get AI-powered sprint recommendations\n\n#### Example prompts\n\nTry these prompts in GitLab Duo Agent Platform Chat:\n\n```text\nList all the unassigned issues in JIRA for project GITLAB\n```\n\n```text\nSuggest the two top issues to prioritize and summarize them. Assign them to me.\n```\n\n### Interactive walkthrough: Project planning\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005462?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Project Planning\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player. js\">\u003C/script>\n\n### Issue triage and creation from code\n\n**Scenario:** While reviewing code, you discover a bug and want to create a Jira issue with relevant context — without leaving your IDE.\n\nThis demo walks you through:\n\n* Identifying a bug while coding\n* Creating a detailed Jira issue via natural language\n* Auto-populating issue fields with code context\n* Linking the issue to your current branch\n\n#### Example prompts\n\n```text\nSearch in JIRA for a bug related to: Null pointer exception in PaymentService.processRefund().\nIf it does not exist create it with all the context needed from the code. Find possible blockers that this bug may cause.\n```\n\n```text\nCreate a new branch called issue-gitlab-18, checkout, and link it to the issue we just created. Assign the JIRA issue to me and mark it as in-progress.\n```\n\n### Interactive walkthrough: Bug review and task automation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005368?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Bug Review\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Cross-system incident investigation\n\n**Scenario:** A production incident occurs, and you need to correlate information from Jira (incident ticket), GitLab Project Management, your codebase, and merge requests to identify the root cause.\n\nThis demo demonstrates:\n\n* Fetching incident details from Jira\n* Correlating with recent merge requests in GitLab\n* Identifying potentially related code changes\n* Generating an incident timeline\n* Design a remediation plan and create it as a work item in GitLab\n\n#### Example prompts\n\n```text\n\"We have a production incident INC-1 about checkout failures. Can you help me investigate with all available context?\"\n```\n\n```text\nCreate a timeline of events for incident INC-1 including related Jira issues and recent deployments\n```\n\n```text\nPropose a remediation plan\n```\n\n### Interactive walkthrough: Cross-system troubleshooting and remediation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005413?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Cross System Investigation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Troubleshooting\n\nThese are some common setup issues and quick fixes:\n\n| Issue | Solution |\n| ----- | ----- |\n| \"MCP server not found\" | Verify the `mcp.json` file is in the correct location and properly formatted |\n| \"Authentication failed\" | Re-check your OAuth credentials and ensure scopes are correctly configured in Atlassian |\n| \"No Jira tools available\" | Restart VS Code after updating `mcp.json` and ensure MCP is enabled in GitLab |\n| \"Connection timeout\" | Check your network connectivity to `mcp.atlassian.com` |\n\n\u003Cbr/> For detailed troubleshooting, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n\n\n## Security considerations\n\nWhen integrating Jira with GitLab Duo Agent Platform:\n\n* **OAuth tokens** — Make sure credentials remain secure\n* **Principle of least privilege** — Only grant the minimum required Jira scopes\n* **Token rotation** — Regularly rotate your OAuth credentials as part of security hygiene\n\n\n## Summary\n\nConnecting GitLab Duo Agent Platform to different tools through MCP transforms how you interact with your development lifecycle. In this article, you have learned how to:\n\n* **Query issues naturally** — Ask questions about your backlog, sprints, and incidents in natural language.\n* **Create and update issues on all your DevSecOps environment** — File bugs and update tickets without leaving your IDE.\n* **Correlate across systems** — Combine Jira data with GitLab project management, merge requests, and pipelines for complete visibility.\n* **Reduce context switching** — Keep your focus on code while staying connected to project management.\n\nThis integration exemplifies the power of MCP: standardized, secure access to your tools through AI, enabling developers to work more efficiently without sacrificing governance or security.\n\n\n## Read more\n\n* [GitLab Duo Agent Platform adds support for Model Context Protocol](https://about.gitlab.com/blog/duo-agent-platform-with-mcp/)\n\n* [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n\n* [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n\n* [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n\n* [Get started with GitLab Duo Agent Platform: The complete guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)",{"featured":31,"template":13,"slug":755},"extend-gitlab-duo-agent-platform-connect-any-tool-with-mcp",{"header":757,"blurb":758,"button":759,"secondaryButton":764},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":760,"config":761},"Get your free trial",{"href":762,"dataGaName":51,"dataGaLocation":763},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":765},{"href":55,"dataGaName":56,"dataGaLocation":763},{"promotions":767},[768,781,792,804],{"id":769,"categories":770,"header":771,"text":772,"button":773,"image":778},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":774,"config":775},"Get your AI maturity score",{"href":776,"dataGaName":777,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":782,"categories":783,"header":784,"text":772,"button":785,"image":789},"devops-modernization",[726,569],"Are you just managing tools or shipping innovation?",{"text":786,"config":787},"Get your DevOps maturity score",{"href":788,"dataGaName":777,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":793,"categories":794,"header":796,"text":772,"button":797,"image":801},"security-modernization",[795],"security","Are you trading speed for security?",{"text":798,"config":799},"Get your security maturity score",{"href":800,"dataGaName":777,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":802},{"src":803},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":805,"paths":806,"header":809,"text":810,"button":811,"image":816},"github-azure-migration",[807,808],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. 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