To improve retrieval using an LLM, you can leverage its ability to understand and reformulate language. One approach is query expansion or refinement: the LLM can generate alternative or more precise search queries based on the user's original input. For example, if a user searches for "how to fix a car engine noise," the LLM might generate variations like "common causes of engine knocking" or "diagnosing rattling sounds in vehicle engines," which could better align with relevant documents. Additionally, the LLM can infer context from conversational history (e.g., in chatbots) to add missing keywords or disambiguate vague terms. For re-ranking, the LLM can score retrieved results by evaluating their relevance to the query. For instance, it could compute a similarity score between the query and each document's content, or summarize retrieved passages to identify overlap with the user's intent. Tools like cross-encoders (e.g., using a model like BERT) can compare query-document pairs directly for more accurate rankings.
To measure impact, track retrieval quality metrics such as precision@k (proportion of relevant results in the top-k results), recall (coverage of all relevant documents), or normalized discounted cumulative gain (NDCG), which accounts for ranking order. For user-facing applications, monitor engagement metrics like click-through rates, time spent on results, or explicit feedback (e.g., thumbs-up/down). A/B testing is critical: compare the baseline retrieval system (without LLM enhancements) against the LLM-augmented version using the same queries and datasets. For example, if the LLM-generated queries improve precision@10 from 40% to 55%, that indicates a meaningful gain. Latency and computational cost are also key—measure the time added by LLM processing (e.g., query rewriting or re-ranking) to ensure it doesn’t degrade user experience.
Practical implementation requires balancing accuracy and efficiency. For instance, using a smaller LLM like TinyBERT for re-ranking might save latency while still improving results. Testing on domain-specific benchmarks (e.g., MS MARCO for web search) can validate generalizability. Additionally, qualitative analysis—such as having humans rate result relevance—helps identify edge cases where the LLM over-optimizes for certain patterns. For example, an LLM might prioritize technical jargon over layman-friendly explanations, which could hurt usability despite higher metric scores. Iterative testing and combining automated metrics with user studies ensure the improvements align with real-world needs.