You are implementing Retrieval-Augmented Generation (RAG) using Azure AI Search and Azure OpenAI. Users report that when searching for technical product specifications, the system occasionally retrieves outdated product manuals because newer versions share similar titles. What configuration change should you make to improve retrieval accuracy?
Trap 1: Increase the chunk size parameter in the document ingestion…
Larger chunks dilute specific details and can exacerbate retrieval noise rather than resolve versioning confusion.
Trap 2: Decrease the temperature parameter of the Azure OpenAI generation…
Temperature controls generation randomness, not the retrieval phase where outdated documents are being fetched.
Trap 3: Switch the embedding model from text-embedding-ada-002 to…
While text-embedding-3-small is more efficient, simply switching models does not inherently resolve document versioning issues without metadata filters.
- A
Increase the chunk size parameter in the document ingestion pipeline to 4000 tokens
Why wrong: Larger chunks dilute specific details and can exacerbate retrieval noise rather than resolve versioning confusion.
- B
Decrease the temperature parameter of the Azure OpenAI generation model to 0.0
Why wrong: Temperature controls generation randomness, not the retrieval phase where outdated documents are being fetched.
- C
Switch the embedding model from text-embedding-ada-002 to text-embedding-3-small
Why wrong: While text-embedding-3-small is more efficient, simply switching models does not inherently resolve document versioning issues without metadata filters.
- D
Enable semantic search and semantic ranking on the Azure AI Search index
Semantic ranking uses advanced language models to re-rank search results, significantly improving relevancy for technical queries.