RT Avi 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 similar than "Machine learning is fun."This semantic dissimilarity leads to several irrelevant contexts being retrieved during the retrieval step.HyDE solves this issue.The visual below illustrates how this approach dif ...

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 ...

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 similar than "Machine learning is fun."This semantic dissimilarity leads to several irrelevant contexts being retrieved during the retrieval step.HyDE solves this issue.The visual below illustrates how this approach differs from RAG.Here's how it ...