X @Avi Chawla

RAG Challenges & HyDE Solution - Traditional RAG faces challenges due to semantic dissimilarity between questions and answers, leading to irrelevant context retrieval [1] - HyDE addresses this by generating a hypothetical answer using an LLM, embedding it, and using the embedding to retrieve relevant context [2] - HyDE leverages contriever models trained with contrastive learning to filter out hallucinated details in the hypothetical answer [3] HyDE Performance & Trade-offs - Studies indicate HyDE improves retrieval performance compared to traditional embedding models [4] - HyDE implementation results in increased latency and higher LLM usage [4] HyDE Implementation - HyDE involves using an LLM to generate a hypothetical answer (H) for the query (Q) [2] - The hypothetical answer is embedded using a contriever model to obtain embedding (E) [2] - Embedding (E) is used to query the vector database and retrieve relevant context (C) [2] - The hypothetical answer (H), retrieved context (C), and query (Q) are passed to the LLM to produce a final answer [3]