In a RAG pipeline, why is a high recall from the retriever often considered more important than high precision, and what are the trade-offs between these two in practice?

In a RAG (Retrieval-Augmented Generation) pipeline, high recall from the retriever is prioritized over precision because the generator’s ability to produce accurate answers depends heavily on having access to all potentially relevant information. Recall measures how many relevant documents the retriever successfully identifies, while precision focuses on minimizing irrelevant ones. If the retriever misses critical context (low recall), the generator lacks the necessary data to formulate a correct or comprehensive response, even if the few retrieved documents are highly precise. For example, in a question-answering system about medical symptoms, missing a key document describing a rare condition could lead the generator to omit critical details, even if the retrieved documents are 100% precise but incomplete.

The trade-off between recall and precision arises because optimizing for one often reduces the other. A retriever tuned for high recall might return more documents, including irrelevant ones, which increases computational load and introduces noise. For instance, retrieving 20 documents with 15 relevant (75% recall) and 5 irrelevant (75% precision) requires the generator to process extra data but ensures critical information isn’t missed. Conversely, a high-precision retriever returning 5 documents with 4 relevant (80% precision) risks omitting the fifth critical document, leading to incomplete answers. This balance depends on the use case: applications like legal research or medical diagnosis prioritize recall to avoid missing key evidence, while low-stakes chatbots might favor precision to reduce latency.

In practice, developers address these trade-offs by adjusting retrieval parameters. Increasing the number of retrieved documents (e.g., from 5 to 20) boosts recall but lowers precision. Techniques like dense vector search (e.g., using embeddings) improve recall by capturing semantic relevance, while hybrid approaches combining keyword-based methods (e.g., BM25) with vector search can balance both metrics. Post-retrieval reranking or filtering can then improve precision without sacrificing recall. For example, a technical support bot might first retrieve 30 documents broadly related to an error message (high recall), then use a lightweight model to rerank them, ensuring the generator receives the top 5 most precise results. This layered approach mitigates the downsides of prioritizing recall while maintaining response quality.