Understanding AI Context Windows: Why It Matters for Your Code

If you’ve ever had an AI coding assistant “forget” something you told it five minutes ago, or generate code that contradicts what it wrote earlier in the same session, you’ve run into a context window limitation. Understanding how context windows work is the single biggest unlock for getting better results from any AI coding tool. It’s also one of the least discussed topics in the “AI for developers” space.

What Is a Context Window?

A context window is the total amount of text an AI model can “see” at once. Think of it as the model’s working memory. Everything it needs to understand your request, the conversation history, the code you’ve shared, the files it’s read, its own previous responses, all of it has to fit within this window.

Context windows are measured in tokens, which are roughly equivalent to word fragments. A rough rule of thumb: 1 token is about 4 characters in English or about 0.75 words. A 200,000-token context window can hold roughly 150,000 words or about 500 pages of text.

That sounds like a lot. And for a single conversation, it usually is. But when you’re working with code, context fills up faster than you’d expect.

How Context Affects Code Quality

Here’s something I’ve observed consistently: the quality of AI-generated code is directly proportional to the relevance of the context it has. Not the quantity, the relevance. A focused context with exactly the right files produces better code than dumping your entire codebase into the window.

Consider two approaches to the same task:

Approach A (vague, no context):

> Write a function to validate user input

Approach B (specific, with relevant context):

> I need to add input validation to the new endpoint in
  src/routes/users.ts. Check the existing validation in
  src/routes/products.ts for the pattern we use. We use
  zod schemas co-located with routes.

Approach A produces generic code that might work but won’t fit your project. Approach B produces code that follows your patterns, uses your validation library, and fits your file structure. The difference is context.

The Token Economy: Where Your Context Actually Goes

Most developers don’t realize how quickly context gets consumed. Here’s a realistic breakdown of where tokens go in a typical coding session:

• System instructions and tool definitions: 2,000-5,000 tokens (you don’t control this)
• CLAUDE.md or project context file: 500-2,000 tokens
• Each file the AI reads: 500-5,000 tokens per file (a 200-line file is roughly 1,500-2,000 tokens)
• Your prompts: 50-500 tokens each
• AI responses: 500-3,000 tokens each
• Conversation history: Cumulative total of all previous exchanges

A long coding session where the AI has read 10 files and you’ve exchanged 15 messages can easily consume 50,000-80,000 tokens. On a 200K context window, that’s already 25-40% of capacity. And remember, the model needs room for its response too.

Strategies for Managing Context

1. Use Project Context Files Wisely

Your CLAUDE.md (or equivalent) is persistent context that’s loaded every session. Keep it concise and high-value. Don’t put your entire architecture document in there. Instead, include conventions, common commands, and pointers to important files. The AI can always read files on demand; it doesn’t need them pre-loaded.

2. Clear Context Between Tasks

When you finish one task and start another, clear the conversation. Leftover context from a previous task doesn’t help and actively hurts. If you were debugging a database connection issue and now you’re writing a UI component, that database context is noise that could confuse the model.

3. Be Specific About Which Files to Read

Instead of letting the AI explore your codebase freely, point it to exactly the files it needs:

> Read src/services/payment.service.ts and
  src/repos/payment.repo.ts. Then add a refund method
  that follows the same patterns as the existing
  charge method.

This uses context efficiently. The AI reads two relevant files instead of exploring ten to find the right ones.

4. Use Compact or Summary Features

Most AI coding tools have a way to compress conversation history. In Claude Code, the /compact command summarizes the conversation to free up context space. Use this proactively in long sessions, not just when you hit the limit.

5. Structure Large Tasks as Multiple Sessions

If you’re implementing a large feature, don’t try to do it all in one conversation. Break it into focused sessions: one for the database layer, one for the API routes, one for tests. Each session gets fresh, focused context.

Practical Tips for Better Results

• Front-load important context. Information at the beginning and end of the context window gets more attention than information in the middle. Put your most important requirements and constraints early in the conversation.
• Don’t repeat yourself. If you’ve already told the AI about a convention, you don’t need to repeat it every prompt. It’s still in the context window.
• Reference files by path, don’t paste. Instead of copying code into your prompt, tell the AI to read the file. This is often more token-efficient and ensures it gets the complete, current version.
• Watch for signs of context degradation. If the AI starts contradicting its earlier output, forgetting conventions you established, or generating increasingly generic code, your context is probably getting crowded. Time to compact or start fresh.

Context management might sound like a minor optimization, but in practice it’s the difference between an AI assistant that feels like a senior teammate and one that feels like a confused intern. The developers getting the best results from AI tools aren’t writing better prompts; they’re managing context better. It’s the meta-skill that makes everything else work.