To reduce hallucinations in a RAG (Retrieval-Augmented Generation) pipeline, modifications should target both the retrieval and generation stages to ensure the model relies on accurate, relevant context. Here are three key strategies:
1. Enhance Retrieval Quality Improving the relevance of retrieved documents directly reduces the risk of the generator relying on poor context. Start by upgrading the embedding model (e.g., switching from basic models like TF-IDF to dense retrievers like SBERT or OpenAI embeddings) to better capture semantic similarity. Next, implement a re-ranking step using cross-encoders (e.g., MiniLM or BERT-based re-rankers) to score document-query relevance more accurately than cosine similarity alone. For example, retrieve 20 documents with a fast embedding model, then re-rank them to keep the top 5. Additionally, use query expansion techniques like HyDE (Hypothetical Document Embeddings), where the model generates a hypothetical answer first and uses it to refine the search query. This helps align retrieved documents with the intended answer structure.
2. Optimize Prompts for Context Adherence Explicitly instruct the generator to base answers strictly on the provided context. Structure prompts to separate retrieved documents from the question and include guardrails like, "Answer using only the context below. If unsure, state 'I don't know.'" For complex queries, use multi-step prompts that force the model to list supporting evidence from the context before synthesizing an answer. For example, include a step like, "First, identify relevant passages from the documents, then write the final answer." This reduces off-context speculation. Testing different phrasings (e.g., "You are a cautious assistant...") can also nudge the model toward conservative responses.
3. Post-Generation Validation Add a verification layer to check if the generated answer aligns with retrieved documents. For instance, use a Natural Language Inference (NLI) model like DeBERTa to score whether the answer is entailed by the context. Answers below a confidence threshold could trigger a fallback response like "I don’t have enough information." For factual claims, integrate entity validation against a knowledge graph or database to flag inconsistencies (e.g., verifying dates or names). While this adds latency, it’s critical for high-stakes applications. Alternatively, fine-tune the generator on datasets that penalize hallucinations, such as those requiring citations from provided sources, to inherently reduce off-context generation.
By combining stronger retrieval, constrained prompting, and answer validation, the pipeline becomes more robust against hallucinations while maintaining flexibility. For example, a medical QA system might use SBERT for retrieval, a re-ranker to filter irrelevant studies, and an NLI model to ensure treatment recommendations match the latest guidelines. Each layer addresses hallucinations at different stages, balancing accuracy and practicality.