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This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[12,691],"DevOps platform",{},"/en-us/blog/10-ai-prompts-to-speed-your-teams-software-delivery",{"config":695,"title":683,"description":684},{"noIndex":10},"en-us/blog/10-ai-prompts-to-speed-your-teams-software-delivery",[698,699],"aiml","devops-platform","0bJ7UM-gc_iMVJVA9U2-xZ95FoBh6Ua5Fzt2bOHb9dw",[702,712,721,728,737,746,754,763,772],{"content":703,"config":710},{"title":704,"heroImage":705,"category":9,"description":706,"authors":707},"How GitLab uses prompt guardrails to help protect customers","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663918/Blog/Hero%20Images/aipower.jpg","Learn what prompt guardrails are, how they help mitigate security risks, and what unique considerations GitLab has taken into account when implementing them.",[708,709],"David O'Regan","Roger Woo",{"externalUrl":-1,"slug":711},"how-gitlab-uses-prompt-guardrails-to-help-protect-customers",{"content":713,"config":719},{"title":714,"heroImage":715,"category":9,"description":716,"authors":717},"DevSecOps + Agentic AI: Now on GitLab Self-Managed Ultimate on AWS","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659604/Blog/Hero%20Images/Screenshot_2024-11-27_at_4.55.28_PM.png","Start using AI-powered, DevSecOps-enhanced agents in your AWS GitLab Self-Managed Ultimate instance. Enjoy the benefits of GitLab Duo and Amazon Q in your organization.",[718],"Jackie Porter",{"externalUrl":-1,"slug":720},"devsecops-agentic-ai-now-on-gitlab-self-managed-ultimate-on-aws",{"content":722,"config":726},{"title":723,"heroImage":724,"category":9,"description":725,"authors":-1},"AI trends for 2025: Agentic AI, self-hosted models, and more","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665039/Blog/Hero%20Images/display-the-source-article-ai-trends-coming-in-2026-image-0492-1800x945-fy25.png","Discover coming trends in AI for software development, from on-premises model deployments to proactive AI assistants.",{"externalUrl":727,"slug":-1},"https://about.gitlab.com/the-source/ai/ai-trends-for-2025-agentic-ai-self-hosted-models-and-more/",{"content":729,"config":735},{"title":730,"heroImage":731,"category":9,"description":732,"authors":733},"Automating with GitLab Duo, Part 3: Validating testing","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097447/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%284%29_3LZkiDjHLjhqEkvOvBsVKp_1750097447404.png","Discover what test we ran to validate the impact of GitLab Duo on our team’s automated testing – and the results we achieved.",[734],"Byron Boots",{"externalUrl":-1,"slug":736},"automating-with-gitlab-duo-part-3-validating-testing",{"content":738,"config":744},{"title":739,"heroImage":740,"category":9,"description":741,"authors":742},"Modernizing a simple C++ application to Java with GitLab Duo","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659507/Blog/Hero%20Images/AdobeStock_623844718.jpg","Learn how to refactor code from memory unsafe languages to memory safe languages with the help of GitLab's AI capabilities, saving time and effort on application modernization.",[743],"Cesar Saavedra",{"externalUrl":-1,"slug":745},"modernizing-a-simple-c-application-to-java-with-gitlab-duo",{"content":747,"config":752},{"title":748,"heroImage":749,"category":9,"description":750,"authors":751},"Automating with GitLab Duo, Part 2: Complex testing","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099243/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%284%29_3LZkiDjHLjhqEkvOvBsVKp_1750099243011.png","Find out how the GitLab team addressed more complex testing situations using GitLab Duo's AI capabilities, including ensuring that code testing followed standards.",[734],{"externalUrl":-1,"slug":753},"automating-with-gitlab-duo-part-2-complex-testing",{"content":755,"config":761},{"title":756,"heroImage":757,"category":9,"description":758,"authors":759},"Streamline DevSecOps engineering workflows with GitLab Duo","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658912/Blog/Hero%20Images/blog-image-template-1800x945__20_.png","Learn all the ways GitLab Duo's AI capabilities can improve the efficiency of development workflows. Includes in-depth tutorials and demos.",[760],"Abubakar Siddiq Ango",{"externalUrl":-1,"slug":762},"streamline-devsecops-engineering-workflows-with-gitlab-duo",{"content":764,"config":770},{"title":765,"heroImage":766,"category":9,"description":767,"authors":768},"Quickly resolve broken CI/CD pipelines with AI","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097355/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2811%29_78Dav6FR9EGjhebHWuBVan_1750097355230.png","When your CI/CD pipeline fails, it leads to delays, decreased productivity, and stress. AI-powered Root Cause Analysis makes problem-solving faster and smarter.",[769],"Itzik Gan Baruch",{"externalUrl":-1,"slug":771},"quickly-resolve-broken-ci-cd-pipelines-with-ai",{"content":773,"config":779},{"title":774,"heroImage":775,"category":9,"description":776,"authors":777},"How to leverage GitLab Duo for enhanced security reporting","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098339/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%285%29_1iy516k40hwBDChKcUJ2zb_1750098339103.png","Learn how GitLab Duo enables efficient, real-world security reporting for development, operations, and security teams.",[778,708],"Valentine Mairet",{"externalUrl":-1,"slug":780},"how-to-leverage-gitlab-duo-for-enhanced-security-reporting",1772652120552]