Vector search finds the right chunk but ranks it badly and waves through near-garbage that's vaguely on-topic. Reranking fixes that with a second model - a cross-encoder that reads the query and each chunk together instead of comparing two frozen vectors. The whole trick is affording an expensive judge by only running it on the cheap stage's survivors.
Read articleA talk I gave to the Airvet engineering org on working with AI coding agents - the mental model, context engineering, and the Research → Plan → Implement loop that ties them together.
Read articleA talk I gave to the Airvet engineering org as a primer on AI - what GPT and RAG actually are, how neural networks, tokens, and embeddings fit together, and why the difference matters.
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