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 operates on context, not abstraction. The competitive advantage isn't your code - it's how effectively you architect context. Version it. Standardize it. Make it infrastructure. Your requirements, architecture decisions, and domain knowledge belong in markdown files within version control.
LLMs are optimized for markdown - fewer tokens, cleaner parsing, human-readable yet machine-native. Your strategic plans, architecture decisions, and product roadmaps should live in .md files. Word docs are legacy. Notion is transitional. Markdown is the substrate of AI-native organizations.
Requirements are code now - written in a language both humans and AI understand. This is agentic development: your IDE becomes a thinking partner. Context evolves with your code in the repo, not in disconnected wikis that decay with every hotfix. Junior engineers write code. Senior engineers architect context.
"The fastest-moving teams aren't winning by writing better prompts. They're winning by recognizing a fundamental shift: AI thrives on well-structured context. SDLC.md helps you architect that context as a first-class concern - standardized, versioned, and integrated with your development workflow."
Built by developers who believe context is the new competitive advantage in AI-native development.
We are passionate about helping teams understand that AI doesn't just need code - it needs context. The best codebases are those where knowledge is structured, versioned, and accessible. Markdown files are not just documentation - they are executable specifications that AI can understand and act upon.
Our goal is to show the world that .md files are infrastructure. When you treat context as a first-class concern - versioned in git, reviewed in pull requests, tested for effectiveness - you unlock a new level of development velocity. This is the future of software development: where humans architect context and AI executes implementation.
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.
Have feedback? Found a bug? Want to contribute? We'd love to hear from you.