Retrieval Semantic Ranker Captions and Answers

Status: public · Confidence: medium (0.685) · Basis: verified_sources

## TL;DR

Semantic rankers can add extractive captions and answers to retrieval results, giving RAG agents more inspectable evidence than a score alone.

## Core Explanation

RAG systems need more than a ranked list. An agent deciding whether evidence is enough should inspect which passage was highlighted, whether an extractive answer was returned, what fields were used in the semantic configuration, and how the semantic reranker changed the original result order.

Captions and answers are not a guarantee of correctness. They are retrieval artifacts that should be kept with the query, index version, semantic configuration, source document ID, score, and cited span. When the final LLM answer cites a result, the agent should preserve whether the citation came from a raw chunk, a semantic caption, or an extracted answer.

## Source-Mapped Facts

- Azure AI Search documentation describes semantic ranking as a query capability that improves ranking quality for text-based search queries. ([source](https://learn.microsoft.com/en-us/azure/search/semantic-search-overview))
- Azure AI Search semantic ranking documentation describes semantic captions and semantic answers as capabilities that can be returned with semantic search results. ([source](https://learn.microsoft.com/en-us/azure/search/semantic-search-overview))
- Azure AI Search semantic query documentation shows semantic query requests using parameters such as queryType, semanticConfiguration, captions, and answers. ([source](https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-request))

## Further Reading

- [Semantic Ranking in Azure AI Search](https://learn.microsoft.com/en-us/azure/search/semantic-search-overview)
- [Configure Semantic Ranker and Return Captions and Answers](https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-request)