Context Engineering Is Knowing When to Start Over

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The people I work with know I keep Claude’s memory turned off. The same goes for ChatGPT. I’ve written before about why: Memory can be useful, but it can also bring old preferences and irrelevant instructions into a conversation where they don’t belong.

Turning memory off doesn’t entirely solve the problem. Even within a single thread, context can accumulate in ways that make the model’s responses worse—a problem known as context rot. It might keep following instructions you’ve moved on from, build on an earlier mistake, or overlook a crucial detail buried in the conversation.

That’s why I start a fresh thread for every task. If the task changes, I open a new chat—even if it’s for the same project I was working on five minutes ago.

In my forthcoming book, Context Engineering with DSPy, I explore the seven ways in which context goes wrong and what to do about it. The book is written for engineers building AI products, but I’ve adapted the examples that follow—inspired by Drew Breunig’s “How Long Contexts Fail”—for anyone who uses AI chat. They’ll help you recognize when a conversation has gone off track and restart without losing useful work.