To determine whether a RAG system's poor performance stems from retrieval or generation, start by isolating and evaluating each component. Retrieval issues occur when the system fails to fetch relevant context, while generation problems arise when the model cannot synthesize accurate answers from good context. Here's how to diagnose them:
1. Evaluate Retrieval Accuracy Use retrieval-specific metrics like recall@K (the proportion of relevant documents retrieved in the top K results) and precision@K (how many of the top K documents are relevant). For example, if your system retrieves 10 documents (K=10) but only 2 are relevant, recall@10 is low. This indicates the retriever isn’t finding adequate context. To calculate this, you’ll need a labeled dataset of queries paired with ground-truth relevant documents. Tools like FAISS or Elasticsearch can log retrieval outputs for analysis. If recall is consistently low, optimize the retriever (e.g., fine-tuning embeddings, improving chunking strategies, or expanding the search space).
2. Test the Generator in Isolation Feed the generator manually curated, relevant context (bypassing the retriever) and assess output quality. For example, if the query is “Explain quantum computing,” provide a textbook passage on the topic and see if the answer is coherent. If outputs improve, the retriever is the bottleneck. If they remain poor, the generator may lack domain knowledge, mishandle context, or produce hallucinations. Metrics like BLEU, ROUGE, or human evaluation can quantify this. Additionally, check if the generator follows instructions (e.g., “answer based on the provided context”) to rule out prompt misalignment.
3. Analyze Failure Patterns Look for consistent errors. For instance:
- Retrieval failures: Answers contain factual errors and retrieved documents lack correct information.
- Generation failures: Retrieved documents are correct, but answers misrepresent them (e.g., contradicting context). For example, in a medical QA system, if retrieved papers state “Drug X treats Condition Y” but the answer says “Drug X is ineffective,” the generator is faulty. Conversely, if the retriever returns unrelated studies about Drug Z, retrieval is the issue. Logging intermediate outputs (retrieved context + generated answer) is critical for this analysis.
By systematically isolating components and using targeted metrics, you can pinpoint whether to focus on improving retrieval (e.g., better embedding models) or generation (e.g., model fine-tuning or prompt engineering).