diff --git a/docs/docs.json b/docs/docs.json index e57798880ba..d6469ae2f23 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -233,6 +233,7 @@ "guides/codebase-documentation-awareness", "guides/cli", "guides/continuous-ai", + "guides/continuous-ai-readiness-assessment", "guides/plan-mode-guide", "guides/ollama-guide", "guides/instinct", diff --git a/docs/guides/codebase-documentation-awareness.mdx b/docs/guides/codebase-documentation-awareness.mdx index a79805a3445..d061bce98e0 100644 --- a/docs/guides/codebase-documentation-awareness.mdx +++ b/docs/guides/codebase-documentation-awareness.mdx @@ -1,11 +1,10 @@ --- -title: How to make your agent aware of codebases and documentation +title: How to Make Your Agent Aware of Codebases and Documentation description: Learn how to give your AI agent access to codebases and documentation for more context-aware assistance keywords: [agent, codebase, documentation, MCP, context, RAG, tools] +sidebarTitle: Codebase and Documentation Awareness --- -# How to make your agent aware of codebases and documentation - AI coding agents work best when they understand the context of your project. This guide shows you how to give your agent access to codebases and documentation, making it more helpful and accurate. ## Make your agent aware of your open codebase @@ -28,18 +27,21 @@ Rules guide your agent's behavior and understanding. Place markdown files in `.c # Project Architecture This is a React application with: + - Components in `/src/components` - API routes in `/src/api` - State management using Redux in `/src/store` ## Coding Standards + - Use TypeScript for all new files - Follow the existing naming conventions - Write tests for all new features ``` - Place rules files at different levels of your project hierarchy to scope when they trigger + Place rules files at different levels of your project hierarchy to scope when + they trigger Learn more about [rules configuration](/customize/deep-dives/rules). @@ -60,6 +62,7 @@ Create rules that point to external codebases: # External Dependencies Our authentication system is based on: + - [Auth.js documentation](https://authjs.dev/) - [Example implementation](https://github.com/nextauthjs/next-auth-example) @@ -76,6 +79,7 @@ Add rules to guide CLI usage: # Repository Access You can use the `gh` CLI to: + - Search for issues: `gh issue list --repo owner/repo` - View pull requests: `gh pr list --repo owner/repo` - Clone repositories: `gh repo clone owner/repo` @@ -86,6 +90,7 @@ You can use the `gh` CLI to: [DeepWiki MCP](https://hub.continue.dev/deepwiki/deepwiki-mcp) lets your agent explore any public GitHub repository. Once configured, your agent can explore repositories like: + - "Explore the React repository structure" - "Find how authentication is implemented in NextAuth.js" @@ -115,6 +120,7 @@ Guide your agent to relevant documentation: # Documentation Resources For framework-specific questions, refer to: + - React: https://react.dev/reference/react - Next.js: https://nextjs.org/docs - Tailwind CSS: https://tailwindcss.com/docs @@ -127,6 +133,7 @@ Always cite documentation when explaining concepts. [Context7 MCP](https://hub.continue.dev/upstash/context7-mcp) enables your agent to search and retrieve information from public documentation: Your agent can then answer questions like: + - "How do I use React hooks?" - "What's the syntax for Tailwind CSS animations?" @@ -142,6 +149,7 @@ Create rules that reference internal resources: # Internal Documentation Our team documentation is available at: + - API Documentation: https://internal.docs/api - Architecture Guide: https://internal.docs/architecture - Deployment Process: https://internal.docs/deployment diff --git a/docs/guides/continuous-ai-readiness-assessment.mdx b/docs/guides/continuous-ai-readiness-assessment.mdx new file mode 100644 index 00000000000..f72e5dd8550 --- /dev/null +++ b/docs/guides/continuous-ai-readiness-assessment.mdx @@ -0,0 +1,309 @@ +--- +title: Assessing Your Team's Readiness for Continuous AI +description: Complete framework to evaluate team readiness for Continuous AI adoption with maturity levels, assessment criteria, and implementation roadmap. +sidebarTitle: Continuous AI Readiness Assessment +--- + + + **TL;DR:** Use this assessment framework to determine if your team is ready to + move from individual AI tool usage to automated Continuous AI workflows. + Covers technical infrastructure, processes, culture, and organizational + support. + + +## Assessing Continuous AI Readiness + +Continuous AI can dramatically improve development velocity and code quality, but successful implementation requires careful evaluation across four key dimensions. + + + Rushing into Continuous AI without proper foundations leads to frustration and + failed initiatives. Use this framework to identify gaps before scaling. + + +### 1. Identify Your Current Maturity Level + +Determine where your team falls on the Continuous AI maturity spectrum: + + + + Developers use AI tools inconsistently with highly variable results. + + **Characteristics:** + - High rejection rates of AI-generated code (>50%) + - No shared standards or prompting rules + - AI tools lack context about your codebase + - Ad-hoc usage without team coordination + + + + + AI is systematically integrated into team workflows and CI/CD pipelines. + +**Characteristics:** + +- Consistent adoption across 80%+ of team members +- AI integrated into code reviews and deployment processes +- Documented standards for prompts and tool usage +- Basic metrics tracking AI impact + + + + + Certain development processes run autonomously with minimal human oversight. + + **Characteristics:** + - Human intervention rates below 15% + - Robust monitoring and automated rollback systems + - Measurable ROI from automation initiatives + - Advanced context awareness and learning loops + + + + +### 2. Evaluate Readiness Across Four Key Dimensions + +Assess your team's strengths and potential risks across these critical areas: + + + + **Key Questions:** + - Do our development tools integrate reliably? + - Can we measure AI effectiveness and impact? + - Are security policies compatible with AI workflows? + + **🟢 Green Flags:** + - Stable tool integrations with >99.5% uptime + - Comprehensive monitoring and observability + - Security policies that support AI tool usage + - Automated testing and deployment pipelines + + **🔴 Red Flags:** + - Frequent integration breakdowns + - No performance tracking or metrics + - Restrictive security policies blocking AI tools + - Manual deployment processes + + + + + **Key Questions:** + - Are our development workflows consistent and documented? + - Do we have quality gates and review processes? + - Can we reproduce builds and deployments reliably? + + **🟢 Green Flags:** + - Clear coding standards and style guides + - Automated CI/CD with quality gates + - Documented, repeatable processes + - Consistent code review practices + + **🔴 Red Flags:** + - Inconsistent code reviews + - Ad-hoc deployment processes + - "Works on my machine" culture + - Undocumented tribal knowledge + + + + + **Key Questions:** + - Are developers open to adopting new AI-powered workflows? + - How does the team handle experimentation and failure? + - Do team members collaborate effectively on new initiatives? + + **🟢 Green Flags:** + - High curiosity and willingness to experiment + - Collaborative problem-solving culture + - Constructive feedback and learning mindset + - Active knowledge sharing practices + +**🔴 Red Flags:** + +- Strong resistance to workflow changes +- Blame culture around mistakes +- Perfectionism blocking experimentation +- Siloed work with minimal collaboration + + + + + **Key Questions:** + - Does leadership provide budget and resources for AI initiatives? + - Is there tolerance for experimentation and learning? + - Are expectations realistic for ROI timelines? + + **🟢 Green Flags:** + - Executive buy-in and strategic alignment + - Dedicated budget for training and tools + - 3-6 month ROI expectations + - Support for calculated risk-taking + + **🔴 Red Flags:** + - Pressure for immediate ROI (weeks) + - No allocated budget for AI initiatives + - High risk aversion culture + - Lack of leadership engagement + + + + +### 3. Critical Warning Signs + + + **Stop and address these issues before scaling Continuous AI:** + + + + + - Builds breaking regularly (>5% failure rate) + - Unstable deployments or rollback frequency >10% + - No monitoring or observability systems + - Critical security policy conflicts + + + + + - More than 30% of team opposed to AI tools + - No established feedback or learning culture + - History of failed automation initiatives + - Resistance to changing existing workflows + + + + + - Inconsistent development workflows + - No quality gates or review processes + - Manual deployment and testing processes + - Lack of documentation and standards + + + + + - Leadership expecting ROI in weeks vs months + - No allocated budget for AI initiatives + - High pressure, low experimentation tolerance + - Lack of strategic alignment on AI adoption + + + + +### 4. Implementation Roadmap + +Based on your assessment results, follow this step-by-step approach: + + + + Document current performance across key areas: + + - Development velocity (story + points, cycle time) + - Code quality metrics (bug rates, technical debt) + - Review times and approval rates + - Developer satisfaction and productivity + scores + + + + + Choose one high-impact, low-risk workflow to automate first: + + - **Code Review:** Automated analysis and suggestions + - **Documentation:** Auto-generated API docs and README updates + - **Testing:** Automated test generation and maintenance + - **Refactoring:** Systematic code improvement suggestions + + + + + Create and document consistent practices: + - AI tool selection and configuration guidelines + + - Prompting standards and best practices + + - Quality gates and review processes + + - Security and compliance requirements + + + + + Run controlled experiments with success criteria: + - Start with 2-3 team members for 2-4 weeks + - Track metrics against baseline performance + - Gather qualitative feedback on developer experience + - Document lessons learned and optimization opportunities + + + + + Expand successful pilots across the organization: + - Roll out to additional team members gradually + - Implement monitoring and alerting systems + - Establish feedback loops for continuous improvement + - Plan next automation targets based on results + + + + + + + Comprehensive explanation of maturity levels and organizational readiness + factors + + + + Technical implementation details and best practices for Continuous AI + workflows + + + +## Quick Assessment Checklist + + + **Ready to get started?** Use this quick checklist to gauge your immediate + readiness: + + +**Technical Foundation (Score: \_\_\_/4)** + +- Stable CI/CD pipelines with \<5% failure rate +- Monitoring and observability systems in place +- Security policies support AI tool integration +- Development environment standardization + +**Process Maturity (Score: \_\_\_/4)** + +- Documented coding standards and review processes +- Consistent deployment and rollback procedures +- Quality gates and automated testing +- Regular retrospectives and process improvement + +**Team Culture (Score: \_\_\_/4)** + +- \<30% resistance to AI tool adoption +- Active experimentation and learning culture +- Collaborative problem-solving approach +- Constructive feedback and knowledge sharing + +**Organizational Support (Score: \_\_\_/4)** + +- Leadership buy-in and strategic alignment +- Dedicated budget for AI initiatives and training +- 3-6 month ROI expectations (not weeks) +- Support for calculated risk-taking + +--- + +**Overall Readiness Score: \_\_\_/16** + +- **12-16:** Ready to begin Continuous AI implementation +- **8-11:** Address gaps in 1-2 areas before scaling +- **\<8:** Focus on foundational improvements first diff --git a/docs/guides/overview.mdx b/docs/guides/overview.mdx index 3a609ac155d..19b639758c5 100644 --- a/docs/guides/overview.mdx +++ b/docs/guides/overview.mdx @@ -13,6 +13,7 @@ description: "Comprehensive collection of practical guides for Continue includin - [Continuous AI: A Developer's Guide](/guides/continuous-ai) - Integrating AI into development workflows - [How to Use Continue CLI (cn)](/guides/cli) - Command-line interface for Continue +- [Continuous AI Readiness Assessment](/guides/continuous-ai-readiness-assessment) - Evaluate team readiness for Continuous AI adoption ## What Advanced Tutorials Are Available