What Is Context Engineering? The Complete Guide

Context engineering is the practice of designing systems where AI has the information it needs, when it needs it, to produce useful output.

If prompt engineering is writing a good question, context engineering is setting up the entire conversation so the AI understands who you are, what you’re building, and how you work.

This isn’t just a buzzword. It’s becoming the core skill for AI-assisted development.

Why Context Engineering Matters

Every time you start a conversation with Claude, GPT, or any AI coding tool, you’re working with an expert who has amnesia. They know everything about programming in general, but nothing about:

• Your codebase
• Your patterns and conventions
• Your tech stack and versions
• What you’ve already tried
• Why certain decisions were made

The AI is doing its first day on the job. Every. Single. Session.

Context engineering solves this. Instead of re-explaining your world each time, you build systems that automatically provide the right context.

The Three Layers of Context

I think about context in three layers:

1. Project Context (Static)

This is your CLAUDE.md, your README, your architecture docs. It doesn’t change session to session.

What goes here:

• Tech stack and versions
• Folder structure and conventions
• Code style rules
• Common patterns (“we always do X this way”)
• What NOT to do (“never use Y library, we had issues”)

2. Task Context (Per-session)

This is what you’re trying to accomplish right now. The more specific, the better.

What goes here:

• What you’re building and why
• Acceptance criteria
• Related files to reference
• Edge cases to handle
• How this connects to existing code

3. Feedback Context (Dynamic)

This is what you learn as you work. Corrections that shape everything that follows.

What goes here:

• “That approach won’t work because…”
• “Use X pattern instead, like we do in Y file”
• “Good, but also handle the case where…”

Context Engineering vs Prompt Engineering

Prompt engineering asks: “How do I phrase this request?”

Context engineering asks: “What systems do I need so AI always has the right information?”

Practical Starting Points

1. Write a CLAUDE.md

Take an hour. Document your stack, your conventions, your patterns. Put it in your project root.

# CLAUDE.md

## Stack
- Next.js 14 with App Router
- TypeScript (strict mode)
- Prisma for database
- Tailwind CSS

## Conventions
- Components in src/components/
- API routes in src/app/api/
- Use camelCase for variables
- Always use TypeScript interfaces, not types

## Patterns
- Repository pattern for data access
- Server components by default
- Client components only when needed

2. Spec Before You Build

Before any non-trivial feature, write a spec:

## Feature: User Authentication

### What
Add email/password authentication to the app.

### Why
Users need accounts to save their work.

### Acceptance Criteria
- [ ] User can register with email/password
- [ ] User can log in
- [ ] User can log out
- [ ] Session persists across browser refresh
- [ ] Invalid credentials show error message

### Edge Cases
- Duplicate email registration
- Expired session handling
- Password reset flow (v2)

3. Build Verification Systems

You can’t read every line of AI output at scale. Build automated checks:

• Type checking catches structural errors
• Tests validate behavior
• Linting enforces conventions
• Spec-based validation confirms requirements

Context Engineering at Scale

When you’re running multiple AI agents in parallel, context engineering becomes critical.

Each agent needs:

• The same project context (shared CLAUDE.md)
• Different task context (their specific assignment)
• Isolated feedback context (their own conversation)

This is where the “systems that build systems” philosophy comes in. You’re not just using AI—you’re designing how AI uses information.

The Future

A year from now, “context engineering” will probably be a job title. The developers who figure this out now will be building the systems everyone else uses later.

The ones who don’t will be wondering why they keep getting mediocre output while everyone else ships 10x faster.

Context is the new code. Learn to write it well.

Want to Go Deeper?

If you want structured guidance on context engineering for your team, I run workshops that compress this learning into a single day. Your team leaves with working CLAUDE.md files, custom commands, and real workflows—not just concepts.

Further Reading

• Context Is the New Code - Why the paradigm has shifted
• IDEs Are Dead - Where development tooling is heading
• I Learned to Code in the AI Era - My journey with AI-first development