transcript
show notes
Bigger context windows were supposed to solve everything, but research on “lost in the middle” and context rot shows that AI can have the right answer in front of it and still miss it. In this episode, host Emily Laird explains context engineering, which means deciding what information, examples, and history an AI system actually needs to do the job. She covers why one model’s accuracy fell from over 99 percent to roughly 70 percent as its inputs grew, why more examples can still help, and why memory is not the same as context. You also get the Context Audit, a four-step method for cutting the clutter and testing whether your AI is actually using what you give it.
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