What are the best practices for optimizing vector search accuracy when building a knowledge base for our employees who are not technical?
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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 building a knowledge base for our employees who are not technical?
1 real search · 1 real-search pass
best practices vector search retrieval accuracy chunking metadata hybrid search reranking evaluation RAG official documentationGoogle ↗
OBSERVEDComparison
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
best practices vector search retrieval accuracy chunking metadata hybrid search reranking evaluation RAG official documentation Google ↗
- RAG and Generative AI - Azure AI Search · learn.microsoft.com
- Develop a RAG Solution on Azure - Information-Retrieval Phase - Azure Architecture Center · learn.microsoft.com
- architecture-center/docs/ai-ml/guide/rag/rag-information-retrieval.md at main · MicrosoftDocs/architecture-center · github.com
- AI Search retrieval quality guide - Azure Databricks · learn.microsoft.com
- Design and Develop a RAG Solution on Azure - Azure Architecture Center · learn.microsoft.com
- RAG Use Cases for Developers · microsoft.com