Real ChatGPT answer · index seedzjRVVigmvAUcompleteB2B software · AI and automation toolsinformational
What are the best practices for optimizing vector search accuracy when dealing with large historical documents from the nineteen eighties?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the best practices for optimizing vector search accuracy when dealing with large historical documents from the nineteen eighties?
1 real search · 1 real-search pass
vector search retrieval best practices chunking hybrid search reranking metadata filters OCR historical scanned documents OCR quality embeddingsGoogle ↗
OBSERVEDInstruction
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
vector search retrieval best practices chunking hybrid search reranking metadata filters OCR historical scanned documents OCR quality embeddings Google ↗
- Develop a RAG Solution on Azure - Information-Retrieval Phase - Azure Architecture Center · learn.microsoft.com
- Query an AI Search index - Azure Databricks · learn.microsoft.com
- AI Search retrieval quality guide - Azure Databricks · learn.microsoft.com
- Improve RAG chain quality - Azure Databricks · learn.microsoft.com