RT Avi Chawla (@_avichawla)RAG vs. CAG, clearly explained!RAG is great, but it has a major problem:Every query hits the vector database. Even for static information that hasn't changed in months.This is expensive, slow, and unnecessary.Cache-Augmented Generation (CAG) addresses this issue by enabling the model to "remember" static information directly in its key-value (KV) memory.Even better? You can combine RAG and CAG for the best of both worlds.Here's how it works:RAG + CAG splits your knowledge into two ...

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/VFSWzmhNL9Avi Chawla (@_avichawla):RAG vs. CAG, clearly explained!RAG is great, but it has a major problem:Every query hits the vector database. Even for static information that hasn't changed in months.This is expensive, slow, and unnecessary.Cache-Augmented Generation (CAG) addresses this issue by https://t.co/VPImg6xzfo ...

RAG vs. CAG, clearly explained!RAG is great, but it has a major problem:Every query hits the vector database. Even for static information that hasn't changed in months.This is expensive, slow, and unnecessary.Cache-Augmented Generation (CAG) addresses this issue by enabling the model to "remember" static information directly in its key-value (KV) memory.Even better? You can combine RAG and CAG for the best of both worlds.Here's how it works:RAG + CAG splits your knowledge into two layers:↳ Static data (poli ...