10 techniques to improve RAG accuracy

Retrieval-augmented generation (RAG) is a technique that combines large language models (LLMs) with domain-specific knowledge to improve factual accuracy and reduce hallucinations in AI-generated outputs. The process involves enhancing retrieval strategies through methods such as hybrid search, tuning Hierarchical Navigable Small World (HNSW) indices, and optimizing document chunking. Fine-tuning embeddings and LLMs for specific domains can increase precision by aligning with nuanced language and domain-specific requirements. Semantic caching and long-term memory management ensure efficient and consistent responses, particularly in stable knowledge bases or multi-turn dialogues. Additional techniques like query transforms and re-ranking help refine and prioritize retrieved data, while employing an LLM as a judge can evaluate the faithfulness of responses. Redis supports these methods through its AI stack, including the Redis Query Engine, enabling scalable and efficient experimentation and implementation.