In a RAG (Retrieval-Augmented Generation) pipeline, high recall from the retriever is prioritized because the generator’s ability to produce accurate and comprehensive answers depends on having access to all potentially relevant information. If the retriever misses critical context (low recall), the generator cannot compensate, leading to incomplete or incorrect responses. For example, in a question-answering system about medical topics, failing to retrieve a key study or guideline might result in an answer that omits vital treatment options. High recall ensures the generator has sufficient material to work with, even if some retrieved documents are irrelevant. Precision—returning only the most relevant documents—is secondary because modern language models can often filter out noise during generation.
The trade-off between recall and precision arises because optimizing for one typically reduces the other. A retriever tuned for high recall (e.g., using a lower similarity score threshold) will return more documents, increasing the chance of including all relevant ones but also introducing irrelevant ones. This forces the generator to process more data, which increases computational costs and latency. For instance, retrieving 100 passages instead of 10 might add 200ms to the pipeline. Conversely, a high-precision retriever (e.g., using stricter thresholds) reduces computational load but risks missing critical information. In applications like legal document analysis, skipping a single relevant precedent due to overly strict retrieval could invalidate the output.
Developers must balance these trade-offs based on use-case constraints. For latency-sensitive applications (e.g., chatbots), limiting retrieved documents to improve speed might justify slightly lower recall. For accuracy-critical tasks (e.g., research assistance), prioritizing recall ensures the generator has the raw material needed for reliable outputs. Techniques like reranking retrieved documents or using hybrid retrieval (combining dense and sparse methods) can mitigate precision loss without sacrificing recall. However, these add complexity. Ultimately, the choice depends on whether the system’s primary risk is incomplete data (favor recall) or inefficiency (favor precision).