Why is my custom internal chatbot hallucinating outdated product specs even though the correct PDF manuals are indexed in our vector database, and how do I fix the retrieval step?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Why is my custom internal chatbot hallucinating outdated product specs even though the correct PDF manuals are indexed in our vector database, and how do I fix the retrieval step?
1 real search · 1 real-search pass
RAG retrieval outdated documents metadata filtering hybrid search reranking contextual retrieval official documentationGoogle ↗
OBSERVEDOther / navigational
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
RAG retrieval outdated documents metadata filtering hybrid search reranking contextual retrieval official documentation Google ↗