In a chat, 'what about its pricing?' returns junk from retrieval — diagnose the failure and fix it

The assistant takes whatever the user just typed, embeds it, and searches the vector index with that embedding. For a standalone question like 'What does the Pro plan cost?' that works, because the sentence fully describes what the user wants. The follow-up 'what about its pricing?' breaks because of the word **its**. That pronoun points back to something said in an earlier turn — maybe a product, a plan, a competitor. By itself, the sentence carries almost no searchable content. So when the system embeds that short, dependent sentence, the resulting query vector points at a vague region of the space that does not describe anything specific. The retriever returns junk, because there is nothing meaningful to match against. The key realization: the retriever only sees the current query string. It has no access to the conversation history where 'its' was defined. The fix is **query contextualization**, also called query rewriting. Before retrieval, run a step that uses the chat history to rewrite the follow-up into a standalone question — 'what about its pricing?' becomes 'What is the pricing of the Enterprise plan?' Now the embedded query actually means something, and retrieval lands on the right chunks.

The failure is a coreference problem at the embedding step. The system embeds the follow-up turn literally — 'what about its pricing?' — and hands that vector to the retriever. But the meaning of that sentence lives in prior turns: **its** is a pronoun referring to an entity named earlier, like a specific product or plan. Stripped of that history, the sentence has almost no retrievable content. So the query vector points at a vague, near-meaningless region of the embedding space, and the retriever pulls back whatever happens to be nearby — junk. The root cause is that retrieval only sees the current query string; it has no window into the conversation where the referent was established. The fix is **query contextualization** — also called query rewriting or condensation. Before retrieval, insert a step that takes the conversation so far plus the new turn and rewrites the follow-up into a standalone, self-contained query. The dependent turn becomes a self-contained one, so a vague follow-up about pricing turns into a full question naming the Enterprise plan. That resolves the coreference, so the embedded query genuinely describes what the user wants and retrieval matches the right chunks. Placement is the detail that decides everything: this rewrite must sit before retrieval, not after. A post-filter or reranker cannot help, because the original query vector already failed to surface anything relevant — there is nothing good in the candidate set to rescue.