A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from...

Microsoft ' s RAG optimization guidance identifies two high-impact, low-cost improvements for retrieval quality that do not require changing the embedding model. First, tuning chunk size and overlap (option A): chunk size determines how much context each retrieved piece contains — too large and irrelevant content dilutes the signal; too small and answers may be split across chunks. Adjusting these parameters requires only re-indexing the documents with zero additional compute cost. Second, implementing a re-ranker (option B): a re-ranker is a cross-encoder model that takes the top-N retrieved chunks and re-scores them based on their specific relevance to the query, significantly improving precision by filtering out contextually irrelevant chunks. Re-rankers add modest compute cost but are far cheaper than changing the embedding model, which would require re-embedding the entire knowledge base. Increasing token limits (option C) and optimizing embedding vector length (option D) do not address retrieval accuracy without an embedding model change.

Microsoft Learn Reference Topic: Optimize RAG pipelines – Chunk size tuning and re-ranking in Azure AI Search and Azure Machine Learning