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]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/DzDoKaIVcZAvi Chawla (@_avichawla):Traditional RAG vs. HyDE, visually explained!RAG is great, but it has a major problem:Questions are not semantically similar to their answers.Consider an example where you want to find context similar to "What is ML?"It is likely that "What is AI?" will appear more https://t.co/oZ7lttsZbG ...

HyDE hands-on guide: https://t.co/iVF79aAAnQ ...

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 to the query and embedding it 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] - The improvement in retrieval performance comes at the cost of increased latency and higher LLM usage [4] HyDE Implementation - HyDE involves using an LLM to generate a hypothetical answer, embedding the answer using a contriever model, querying the vector database, and passing the hypothetical answer, retrieved context, and query to the LLM for the final answer [2]