AI LLM Search Cache Index

The idea, in context

An AI-assisted search experience can combine retrieval, source selection, and generated text. An AI LLM search cache index is a useful design lens for recording the relationship between a question, retrieved material, and the answer that followed. Keep those layers distinct. Reusing a retrieval result does not imply the same final answer, and a retained answer can become unsuitable when the source changes. Record provenance at the point of generation so you can later explain what the response actually relied on.

Define the retrieval boundary

Store the question representation, corpus version, relevant filters, and authorized scope. If your retrieval strategy changes, compare results under a new version instead of mixing them with the earlier system. A repeated question is not enough to establish identical context.

Retain support for the answer

Keep document identifiers and versions alongside any answer you choose to reuse. Distinguish a citation shown to the reader from an internal candidate that the system did not use. This helps reviewers trace the answer to specific evidence.

Plan for new information

A fresh document, a corrected source, or a changed permission can make old retrieval or answer entries unsuitable. Decide which event invalidates which layer. Test updates with a known changed fact so you can observe whether the system surfaces the new material.