Context Engineering Is the Real Skill
AI is bringing back something developers spent years trying to avoid: documentation.
Not for humans. For agents.
For years, we avoided writing docs because nobody read them. Now the most important reader of your documentation isn’t another developer — it’s your AI agent.
I’ve spent the last year building extensively with Claude Code and other coding agents. The biggest misconception I see? People think better prompts lead to better results. They don’t.
Better context leads to better results.
The best AI developers aren’t prompt engineers. They’re context engineers.
Here’s what that looks like in practice. Every coding agent eventually asks the same five questions:
How should I behave?
→ CLAUDE.md / AGENTS.md
Coding standards. Architecture principles. Project rules.
Without this, every session starts with the AI guessing your preferences.
What are we actually building?
→ PRD.md
Requirements tell AI what to build. Context tells AI why it matters. The second produces far better code.
Why was it built this way?
→ ADRs (Architecture Decision Records)
Why FastAPI? Why Supabase? Why not microservices?
Without ADRs, every new session confidently proposes solutions you already rejected weeks ago. This was one of the biggest time-savers for me.
What should I do next?
→ PLAN.md / TASKS.md
Priorities. Dependencies. Current progress. Clear plans outperform clever prompts — every single time.
What have we already learned?
→ LEARNINGS.md
Failed experiments. Production incidents. Performance discoveries.
The most valuable knowledge in a codebase isn’t what worked. It’s what didn’t.
The teams moving fastest with AI aren’t the ones with the biggest context windows. They’re the ones with the best memory systems.
Context engineering is quietly becoming one of the most important skills in software development.
You don’t need all five files on day one. Start with CLAUDE.md and TASKS.md. Add the rest as your project grows.
Prompts matter. Context matters more.