Maximize the effectiveness of your .md context files with these battle-tested strategies for working with AI coding assistants like Claude Code, Cursor, and GitHub Copilot.
Your .md should be a high-level guide with strategic pointers, not a comprehensive manual. Document what your AI consistently gets wrong. If explaining something requires more than 3 paragraphs, the problem is your tooling, not your docs.
Avoid @-mentioning files unnecessarily - it bloats context windows. Instead, sell the AI on when to read: "For database errors, see /docs/db.md" vs embedding the entire file. Save tokens for code, not documentation.
Never say "Never" without offering alternatives. "Don't use --force" leaves AI stuck. Instead: "Prefer --safe-mode. Use --force only in dev with approval." Prescriptive > restrictive.
If you need paragraphs to explain a command, the command is the problem. Build a wrapper script with a better API. Short .md files force codebase simplification. Complexity documented is complexity that should be eliminated.
Avoid /compact - it's opaque and lossy. Simple restart: /clear + /catchup. Complex work: dump state to .md, /clear, resume from file. Document > compact. Always.
For large changes, always use planning mode. Align on approach and define checkpoint reviews before implementation. Planning builds AI intuition about your context needs. Code without planning wastes both your time.
One good example beats three paragraphs of explanation. Instead of describing patterns abstractly, show concrete code. AI learns faster from // Example: than from "The pattern is...". Prefer copy-pasteable snippets.
Context files belong in git with your code. When code evolves, context must evolve. Treat CONTEXT.md changes like code changes - review in PRs, test effectiveness, document breaking changes. Stale context is worse than no context.
Use global (~/.claude/context.md), project (CONTEXT.md), and file-level context. Global for your personal patterns, project for codebase conventions, inline for file-specific nuances. Don't repeat yourself across layers.
Explicitly state what's in-scope and out-of-scope. "Don't modify files in /vendor" or "Test coverage required for /src only". Clear boundaries prevent AI from over-helping or making incorrect assumptions.
Verify AI uses your context. Try /clear + task that should use context. Does AI follow patterns? If not, your context isn't working. Iterate until behavior matches intent. Context untested is context unused.
Context rots faster than code. When you change patterns, update context immediately. Outdated context trains AI on deprecated patterns. Set calendar reminders to review quarterly. Fresh context compounds value.
Context files are infrastructure, not documentation. Your .md should be executable specification - concise, versioned, and tested. Think "API contract for AI" not "reference manual for humans."
Slash commands are shortcuts. Context files are strategy. Commands trigger actions. Context shapes behavior. Master both, but invest in context - it compounds over time while commands stay transactional.
AI coding assistants need structure, not chaos. CodeAI.md transforms your repository into a queryable knowledge graph. Document patterns, conventions, and integration points in markdown. Your AI pair programmer performs best when it understands not just what your code does, but why it exists and how it fits together.
Stop re-explaining your architecture in every prompt. CodeAI.md captures reusable patterns, common workflows, and decision frameworks in structured markdown. Feed your AI assistant context once, reference it forever. Consistency scales. Ad-hoc prompting doesn't.
Your codebase documentation should drive code generation, not just describe it. CodeAI.md bridges the gap between human intent and machine implementation. Document your patterns in markdown, let AI assistants generate consistent, context-aware code. The docs become executable specifications.
"Elite development teams have discovered a secret: the best AI coding assistant is one that deeply understands your specific codebase. CodeAI.md gives you the infrastructure to capture and version that understanding, turning tribal knowledge into permanent competitive advantage."
Built by engineers who understand that AI coding assistants are only as good as the context you provide them.
We believe the future of coding is collaborative - humans architect systems, AI generates implementation. But this only works when your AI assistant deeply understands your codebase. CodeAI.md helps you structure that understanding in markdown files that live alongside your code, evolve with your patterns, and compound in value over time.
Our mission is to help development teams capture their codebase knowledge in a format that is both human-readable and AI-optimal. When your patterns, conventions, and architectural decisions are documented in versioned .md files, every developer and every AI assistant benefits. This is how elite teams scale their expertise.
LLMs parse markdown better than any other format. Fewer tokens, cleaner structure, better results.
Context evolves with code. Git tracks changes, PRs enable review, history preserves decisions.
No special tools needed. Plain text that works everywhere. Documentation humans actually read.
Questions about structuring your codebase for AI? Need help with implementation patterns? Reach out - we're here to help.