Real ChatGPT answer · index seed6P34sdyNquMcompleteB2B software · AI and automation toolsinformational
Why are my vector search results returning irrelevant documents, and what tuning methods won't blow up my monthly cloud bill?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Why are my vector search results returning irrelevant documents, and what tuning methods won't blow up my monthly cloud bill?
1 real search · 1 real-search pass
vector search retrieval quality tuning chunking hybrid search reranking metadata filtering cost optimization vector database documentationGoogle ↗
OBSERVEDReason
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 quality tuning chunking hybrid search reranking metadata filtering cost optimization vector database documentation Google ↗
- Hybrid search · docs.opensearch.org
- Best Practices for Advanced PostgreSQL Vector Workloads · docs.digitalocean.com
- How to Perform Hybrid Search - MongoDB Vector Search · mongodb.com
- architecture-center/docs/ai-ml/guide/rag/rag-information-retrieval.md at main · MicrosoftDocs/architecture-center · github.com
- Develop a RAG Solution on Azure - Information-Retrieval Phase - Azure Architecture Center · learn.microsoft.com
- Create a Hybrid Query - Azure AI Search · learn.microsoft.com
- Create a Hybrid Query - Azure AI Search · learn.microsoft.com