To ensure a RAG system’s answer fully addresses all parts of a user’s query, start by refining the retrieval and generation pipeline to explicitly map to the query’s requirements. For example, if a user asks, “What are the benefits, risks, and best practices of X?” the system must retrieve context covering all three aspects and generate a response that addresses each point. This requires a combination of structured retrieval, prompt engineering, and post-generation validation.
First, improve retrieval precision by decomposing multi-part queries into sub-questions. For instance, split “benefits, risks, best practices” into three distinct search queries. Use hybrid retrieval methods (e.g., combining keyword and vector search) to ensure diverse coverage. Tools like LangChain’s MultiQueryRetriever can automate this by generating sub-questions from the original query. Verify retrieved documents for relevance to each sub-topic using techniques like sentence-window retrieval or reranking. For example, cross-check if retrieved passages mention “cost savings” (benefit), “data leakage” (risk), or “encryption standards” (best practice).
Next, design prompts that explicitly instruct the LLM to address all query components. Use templates like: “Answer the user’s question by covering: 1) [Topic A], 2) [Topic B], 3) [Topic C].” For programmatic validation, implement post-processing checks. For instance, use a smaller model (e.g., Mistral-7B) to evaluate if the generated response contains embeddings matching keywords like “benefit,” “risk,” or “best practice.” Alternatively, create a checklist of required points and flag missing items for regeneration. Tools like Guardrails.ai can enforce this programmatically.
Finally, test iteratively with real-world queries. For example, run a batch of 50 multi-part questions through the pipeline, and measure coverage using metrics like BERTScore to compare generated answers against ground-truth references. If the system misses risks in 30% of cases, refine the retriever’s risk-related keyword filters or add a dedicated risk-detection prompt step. Continuously update the retrieval corpus to fill knowledge gaps—for instance, adding whitepapers on emerging risks if the system underperforms on that subtopic. This cycle ensures the RAG pipeline evolves to handle complex, multi-faceted queries reliably.