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.
Business requirements scattered across Jira, emails, and meetings create ambiguity. Word.md consolidates user stories, acceptance criteria, and domain glossaries in markdown. Your AI assistants can reference the single source of truth. Product context becomes infrastructure, not afterthought.
Product decisions need documentation that evolves with understanding. Word.md captures requirements, constraints, and trade-offs in version-controlled markdown. Stakeholders review in pull requests. AI assistants help maintain consistency. Everyone works from the same definition of done.
Your business domain is complex - capture it properly. Word.md documents domain concepts, business rules, and terminology in structured markdown. New team members get instant access. AI assistants understand your business context. Tribal knowledge becomes organizational asset.
"Product success depends on shared understanding. Word.md transforms ephemeral conversations into permanent, versioned context - ensuring that business requirements, stakeholder intentions, and domain knowledge persist throughout the development lifecycle."
Built by product teams who believe requirements are too important to live in disconnected tools.
We are on a mission to help product teams recognize that requirements scattered across Jira, emails, and meetings create ambiguity and delay. Business context deserves the same rigor as code - versioned, reviewed, and accessible. When requirements live in .md files alongside your code, product and engineering move at the same velocity.
Our goal is to demonstrate that product documentation can be infrastructure. User stories, acceptance criteria, and domain glossaries in markdown become the source of truth that both humans and AI reference. This is how aligned teams ship faster - shared understanding captured in shared format.
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.
Need help structuring product requirements? Want to discuss business context strategies? We're happy to chat.