Can you share some tips on optimizing vector search accuracy when the company knowledge base has thousands of messy PDF files?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can you share some tips on optimizing vector search accuracy when the company knowledge base has thousands of messy PDF files?
1 real search · 1 real-search pass
enterprise RAG PDF ingestion layout-aware parsing hybrid search reranking evaluation best practices 2025Google ↗
OBSERVED
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.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
enterprise RAG PDF ingestion layout-aware parsing hybrid search reranking evaluation best practices 2025 Google ↗
- Develop a RAG Solution on Azure - Information-Retrieval Phase - Azure Architecture Center · learn.microsoft.com
- RAG and Generative AI - Azure AI Search · learn.microsoft.com
- Production RAG Evaluation: Keyword, Vector, SQL, or Hybrid Search? · blogs.oracle.com
- RAG overview · learn.microsoft.com
- information retrieval guidance · learn.microsoft.com