To reduce hallucinations in LLM responses using semantic search, you need to ground the model’s outputs in verified data by integrating retrieval mechanisms that prioritize factual accuracy. Semantic search allows the LLM to query a structured knowledge base (like a vector database) before generating a response, ensuring the output aligns with reliable sources. This approach limits the model’s reliance on internal memorized patterns, which can lead to incorrect or fabricated claims.
Start by building a high-quality knowledge base containing domain-specific, verified information. For example, a medical chatbot might use research papers or clinical guidelines stored as vector embeddings. When a user asks a question, the system first performs a semantic search across this database to find the most relevant documents or passages. Tools like FAISS, Elasticsearch, or Pinecone can efficiently retrieve data by comparing the semantic similarity between the user’s query and the stored embeddings. The LLM then uses this retrieved context to generate an answer, significantly reducing the chance of inventing facts. For instance, if a user asks, “What’s the recommended treatment for early-stage Lyme disease?” the system retrieves CDC guidelines from the database, ensuring the response cites specific antibiotics like doxycycline instead of guessing.
To implement this effectively, structure the LLM’s prompt to explicitly reference the retrieved data. For example, use a template like: “Based on [retrieved context], the answer is…” This forces the model to stick to the provided information. You can also filter low-confidence search results—if the top matches from the database have low similarity scores, the system can respond with “I don’t have enough information” instead of risking a hallucination. Additionally, validate the approach by testing edge cases. If a user asks, “Can sunlight cure COVID-19?” and the knowledge base contains no supporting evidence, the semantic search returns nothing, and the LLM should refuse to answer. Regularly update the knowledge base and fine-tune retrieval thresholds to maintain accuracy as new data emerges.