Context Is the New Code

· 8 min read
#ai-coding#paradigm-shift#context-engineering#productivity

Most developers are still thinking about AI wrong.

They open Claude or Cursor, type “build me a login form,” get mediocre output, and conclude that AI coding tools are overhyped.

Here’s what they’re missing: the AI isn’t the problem. The context is.

The Shift Nobody Talks About

For decades, being a good developer meant one thing: writing good code. You learned syntax, memorized patterns, built muscle memory. The better you were at translating thoughts into code, the more valuable you became.

That era is ending.

Today, the models are already better at writing code than most developers. Claude, GPT, Gemini — they’ve seen more codebases than any human ever will. They know every pattern, every framework, every edge case.

So why does their output still suck half the time?

Because they don’t know YOUR codebase. They don’t know your patterns. They don’t know that you always put API calls in the services folder, or that you have a custom error handler, or that you use camelCase not snake_case.

Every session, you’re working with a genius-level engineer who’s doing their first day on the job. Every. Single. Time.

The skill now isn’t writing code. It’s giving that genius engineer the context they need to write code YOUR way.

What Context Actually Means

Context isn’t just “here’s some background.” It’s everything the AI needs to make decisions the way you would.

Bad context:

Build me a user authentication system.

Better context:

Build a user authentication system. We use NextJS 14 with app router, Prisma for the database, and NextAuth for auth. Check src/lib/auth.ts for our existing auth utilities. Match the patterns in src/app/api/ for API routes. We never store passwords directly — always use bcrypt.

Even better: The “even better” version isn’t a longer prompt. It’s a CLAUDE.md file, a folder structure the AI can explore, code comments that explain WHY not just WHAT, and spec documents that define expected behavior.

Context is cumulative. Every time you write documentation that explains your patterns, you’re not just helping future developers. You’re training your AI collaborator.

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. It’s the foundation.

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”)

This is the context that makes the AI feel like a team member who’s been onboarded properly.

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

I use spec documents for this. Before I ask AI to build anything non-trivial, I write out what I want. Not pseudo-code — actual specifications. Inputs, outputs, behaviors, edge cases.

(Yes, this takes time upfront. No, it’s not wasted. The spec IS the work.)

3. Feedback Context (Dynamic)

This is what you learn as you work. The AI suggests something wrong — tell it why. It uses the wrong pattern — correct it. These corrections compound.

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…”

The models learn within a session. Corrections early on shape everything that follows.

Why Experienced Developers Struggle

Here’s the uncomfortable truth: developers with 10+ years of experience often have the hardest time with this shift.

Not because they’re less capable. Because they have muscle memory.

When you’ve spent years getting good at writing code, your instinct is to write code. Stopping to write context, specs, and documentation feels like overhead. Feels like you’re slowing down.

But you’re not. You’re just shifting where the work happens.

The 15-year senior engineer who refuses to learn this is a beginner. They’re clinging to a skill that’s rapidly becoming less valuable while ignoring the skill that’s rapidly becoming essential.

Meanwhile, someone who’s never written a line of code can learn to spec work, manage context, and orchestrate AI agents — and ship faster than the veteran who insists on typing every character themselves.

(I would know. I’m one of them. I learned to code in the AI era. It’s the only way I’ve ever worked.)

Context Engineering Is the Job Now

I don’t call what I do “prompt engineering.” Prompts are single-shot. What we’re really doing is context engineering.

Context engineering is:

  • Designing systems where AI has the information it needs, when it needs it
  • Building documentation that serves both humans AND AI
  • Creating specs that define success before writing begins
  • Setting up feedback loops that improve output over time
  • Managing multiple agents with different contexts in parallel

This is the job now. Writing code is what the AI does. Setting the context for that code is what you do.

Practical Starting Points

If you’re new to this, here’s where I’d start:

1. Write a CLAUDE.md (or equivalent) for your project

Take an hour. Document your stack, your conventions, your patterns. The first session after you do this will feel like magic compared to before.

2. Start spec-ing before building

Before your next feature, write a spec. Not a novel — just a clear statement of what you’re building, why, and how you’ll know it’s done. Feed that to the AI before asking it to write anything.

3. Correct loudly

When the AI gets something wrong, don’t just fix it silently. Tell it why it’s wrong. These corrections shape everything that follows.

4. Treat your codebase as AI context

Every comment, every README, every doc you write — ask yourself “would this help an AI understand this code?” If yes, it probably helps humans too. If no, maybe it shouldn’t exist.

The Future Is Already Here

A year from now, “context engineering” will probably be a job title. Two years from now, it’ll be table stakes.

The developers who figure this out now will be building the systems that 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.