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 are most effective with structured context. DevAI.md documents your prompt engineering patterns, context optimization strategies, and AI workflow integrations. Best practices for AI-assisted development become versioned knowledge. Every developer benefits from collective learning.
The quality of AI output depends on the quality of context you provide. DevAI.md captures effective prompting patterns, context structuring techniques, and AI interaction workflows. Transform ad-hoc experimentation into repeatable methodology. Context becomes your competitive advantage.
AI agents need clear instructions and proper context to be effective. DevAI.md documents agent capabilities, integration patterns, and workflow automations in markdown. Your development environment becomes AI-native. Repetitive tasks get automated systematically.
"AI-augmented development requires a new kind of infrastructure - one that captures not just code and requirements, but the patterns and practices for effective AI collaboration. DevAI.md provides that foundation."
Built by AI engineers who understand that prompt engineering is just the beginning.
We believe the next phase of AI-assisted development requires structured context engineering. It's not enough to write good prompts - you need to architect how context flows through your development environment. DevAI.md helps teams document their AI integration patterns, prompt templates, and context optimization strategies in markdown that evolves with their AI workflows.
Our goal is to help development teams transition from ad-hoc AI experimentation to systematic AI integration. When your AI collaboration patterns are captured in versioned .md files, best practices spread across your team, AI assistants become more effective, and your development environment becomes truly AI-native.
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.
Experimenting with AI-assisted development? Want to share your AI integration patterns? Let's learn together.