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How Does Claude's 1M Context Window Boost Coding Speed?

Cowrie
Cowrie
Dev @ Bswen

I was halfway through a complex refactor when my AI assistant forgot everything.

The context window hit its limit. Again.

I had to re-explain the architecture, re-share the critical files, and re-build the mental model I’d spent an hour constructing. This happened three times that day. Each time, I lost 15-20 minutes just getting back to where I was.

The Hidden Cost of Context Limits

The problem wasn’t the token count. The problem was the mental overhead of managing it.

Before Claude’s 1M context window, I spent more time thinking about what to include than actually coding:

  • “Should I add this config file? It might be relevant…”
  • “I’m at 80% capacity. Do I compact now or risk hitting the limit?”
  • “The AI forgot about that utility function from earlier. Let me re-share it…”

This isn’t productive work. This is babysitting.

A developer on Reddit captured it perfectly: “The best part isn’t even the size, it’s not babysitting context anymore. Used to constantly think about what files to keep in scope. Now I just dump everything in and forget about it.”

What Changed with 1M Context

I stopped thinking about context entirely.

Here’s the before and after:

Before:

  • Carefully select 10-20 files
  • Monitor token usage like a hawk
  • Compact context every hour
  • Re-explain architecture after each reset
  • Keep mental notes of what the AI “knows”

After:

  • Dump entire relevant codebase in one go
  • Include tests, configs, docs, everything
  • No compaction for entire sessions
  • Single explanation, retained
  • Focus on the problem, not the tool

Another developer noted: “Our code base is massive, so was always hitting context limits even with subagents and good tooling. Means planning large features actually don’t forget half the things it needs to do.”

This is the real productivity gain. Not more tokens. Less cognitive overhead.

Why This Matters for Large Refactors

I recently worked on a feature that touched 47 files across a monorepo.

With 200K context, I’d have to:

  1. Split the work into chunks
  2. Hope each chunk had enough context
  3. Re-explain the overall architecture for each chunk
  4. Fix things the AI broke because it didn’t see the full picture

With 1M context, I:

  1. Shared all 47 files upfront
  2. Explained the goal once
  3. Let the AI work through the changes
  4. Got consistent results across all files

The refactor took 2 hours instead of a full day.

Common Mistakes I Made

Mistake 1: Still Curating Context

Old habits die hard. My first instinct was still to minimize what I shared.

“Don’t need those test files, probably just noise…”

Wrong. Those test files showed edge cases I would have missed. Now I include everything relevant. The context window can handle it.

Mistake 2: Not Leveraging the Full Codebase

I used to share only the files I was actively editing. But often, the solution to a problem lives in a utility file three directories away.

With 1M context, I share the entire feature area: source files, tests, configs, related utilities. The AI finds connections I didn’t know existed.

Mistake 3: Over-Optimizing Prompts

I spent time crafting the perfect prompt to fit within constraints.

Now I just dump context and ask straightforward questions. The model has all the information it needs. It doesn’t need clever prompting to figure things out.

When 1M Context Actually Helps

This isn’t about blindly throwing files at the AI. It’s about removing a constraint that forced bad behavior.

1M context shines when:

  • Working on features spanning multiple files
  • Debugging issues that require tracing through call chains
  • Refactoring with many moving parts
  • Understanding unfamiliar codebases

It doesn’t help as much for:

  • Simple one-file changes
  • Quick bug fixes with obvious causes
  • Tasks with narrow scope

The Productivity Math

Let’s run the numbers from my experience:

Before:

  • Context curation: 10 minutes
  • First attempt: 15 minutes
  • Context reset: 5 minutes
  • Re-explanation: 10 minutes
  • Second attempt: 20 minutes
  • Total: 60 minutes

After:

  • Dump everything: 2 minutes
  • Single explanation: 5 minutes
  • One attempt: 20 minutes
  • Total: 27 minutes

That’s a 55% time savings. And less frustration.

Summary

In this post, I showed how Claude’s 1M context window changed my workflow. The key insight is that the productivity gain comes from eliminating context management overhead, not from having more tokens. I stopped babysitting context limits and started focusing on actual coding.

If you’re still curating context files or hitting limits mid-session, try dumping everything relevant. The productivity difference is real.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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