To reduce hallucinations in a RAG pipeline, focus on three key areas: improving retrieval quality, refining prompt design, and implementing validation during generation. Each step addresses specific failure points where irrelevant information or ambiguous instructions might lead the model to invent incorrect details.
1. Enhance Retrieval Relevance The foundation of reliable outputs lies in retrieving high-quality context. Start by upgrading the embedding model used for document similarity search. For example, replace generic sentence-transformers with domain-specific models fine-tuned on your data (e.g., a biomedical model for healthcare queries). Implement a two-stage retrieval process: first use a fast vector search to get 50-100 candidates, then apply a reranker like a cross-encoder to rescore the top 20 results. This reduces noise in the context passed to the generator. Additionally, enforce metadata filters (e.g., date ranges or source credibility) during retrieval. For instance, a legal chatbot could prioritize statutes from the last five years while excluding outdated precedents.
2. Design Precise Prompts Explicitly instruct the generator to avoid assumptions. Instead of generic prompts like “Answer the question,” use constrained templates:
"Use ONLY the provided documents below. If the answer isn't found, say 'Not found.'
Documents: [context]
Question: [query]"
Add validation steps within the prompt itself:
1. Check if any document explicitly answers the question.
2. If yes, quote the relevant text.
3. If no, state 'Insufficient data.'
For technical domains, include format requirements like “Cite document IDs for all claims.” Test variations using eval datasets to measure hallucination rates.
3. Validate During/After Generation Configure the language model to prioritize factual consistency. Lower the temperature (e.g., 0.3) to reduce creative guessing, and set max_length to prevent verbose, unfounded explanations. Use constrained decoding libraries like Guidance or LMQL to force the model to reference retrieved document snippets verbatim. Post-generation, run a checker model (e.g., a smaller BERT classifier) to flag unsupported claims by comparing the response to the original context. For critical applications like medical advice, implement human review loops for outputs exceeding confidence thresholds.
By combining these strategies—better context retrieval, explicit instructions, and output validation—developers can systematically reduce hallucinations without requiring full model retraining.
