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.
Generative AI projects succeed or fail based on context quality. GenAI.md documents your model configurations, training data lineage, and evaluation metrics in markdown. Context flows from data to deployment. AI context about AI becomes versioned infrastructure.
Every model iteration represents learning. GenAI.md captures experiment results, parameter decisions, and performance trade-offs in structured markdown. Your AI assistants help analyze patterns across experiments. Institutional memory prevents repeated mistakes.
Deploying generative AI requires more than code - you need deployment pipelines, monitoring strategies, and incident response procedures. GenAI.md documents the operational context that keeps AI systems reliable. From training to production, context is infrastructure.
"Generative AI projects generate massive amounts of context - training data, model configs, experiment results, deployment procedures. GenAI.md helps teams structure this context as versioned markdown, making AI projects manageable and knowledge transferable."
Built by ML engineers who know that the hardest part of AI isn't the model - it's the context.
We understand that generative AI projects generate massive context - training data lineage, model configurations, experiment results, deployment procedures. All of this deserves to be structured as markdown, not scattered across notebooks and Slack. GenAI.md helps ML teams capture their model development journey in a format that both current team members and future AI assistants can learn from.
Our mission is to help AI teams treat their project context as seriously as their model code. When experiment learnings, configuration decisions, and operational procedures live in versioned .md files, AI projects become manageable, knowledge becomes transferable, and teams avoid repeating mistakes. Context is the difference between ML research and ML production.
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.
Building generative AI systems? Need advice on model documentation? Share your challenges - we've been there.