In the realm of Retrieval-Augmented Generation (RAG), one of the trickiest challenges is dealing with ambiguous or unclear user queries. Here’s how to approach this common interview question:
First, clarify the query by using a technique called query rewriting. This involves having the language model rephrase the user's input into a more specific, searchable form. For instance, if a user asks “Tell me about the latest,” the system can infer the topic from context and rewrite it as “latest advancements in AI.”
Second, implement multi-turn conversation management. Maintain a chat history so the system can refer to previous exchanges to disambiguate pronouns or vague references. This prevents the RAG pipeline from losing context.
Third, use a confidence threshold. If the retrieved documents have low relevance scores, the system can ask the user a clarifying question instead of returning a poor answer. This fallback strategy improves user experience.
Fourth, employ hybrid retrieval that combines sparse methods (like BM25) for exact keyword matching with dense embeddings for semantic understanding. This broadens the net and captures relevant documents even when the query is imprecise.
Finally, re-rank retrieved results with a cross-encoder to push the most pertinent passages to the top. This mitigates noise from ambiguous queries.
By combining these techniques—query rewriting, context tracking, confidence checks, hybrid search, and re-ranking—you can robustly handle ambiguity in RAG systems.