One of the stranger things about using large language models is that they never actually tell you when they’re running out of room to think. There’s no pop-up saying, “Hey, you’ve hit the limit.” Instead, the model just quietly gets worse.
Answers get fuzzier. Details get dropped. Logic starts bending.
If you’ve ever wondered why, it comes back to something called input context.
What input context is
Input context is how much information a model can consider at one time. It’s measured in tokens: tiny pieces of text, usually a few characters each.
Every word you type, every code block you paste, and every part of your ongoing conversation all eat into that limit.
What happens when you hit the limit
Once you reach the edge of that limit, older parts of the conversation get internally compressed so everything still fits inside the context window.
Think of it like summarizing your entire chat into a smaller and smaller space each time. That’s context collapse, and it’s why the model slowly feels like it’s forgetting things or getting “dumber.”
Older models hit this wall quickly. Newer ones have much larger windows, but even with a million tokens, it’s still possible to overflow them with long conversations and big codebases.
Practical tips to keep things sharp
- Reset the conversation when answers start drifting
- Re-paste the key context you want the model to use
- Keep prompts focused and avoid unnecessary history
- Send large codebases in smaller, purposeful chunks
Understanding input context won’t stop context collapse entirely, but it will help you recognize it—and help your AI partner stay sharp instead of quietly unraveling at the edges.