RAG Architectures - Industry highlights the distinction between Naive RAG and Agentic RAG [1] - Industry emphasizes visual explanations of RAG architectures [1]

Overview - The content is a recommendation to reshare insightful information about DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) [1] Resource Sharing - Avi Chawla shares tutorials and insights daily on DS, ML, LLMs, and RAGs [1] Topic Focus - The content highlights a clear explanation (with visuals) of Naive RAG vs Agentic RAG [1]

RAG Systems - Agentic RAG systems enhance robustness by aligning individual outcomes with the overall goal [1] - The provided diagram represents one of many possible blueprints for an agentic RAG system [2] - The specific implementation of an agentic RAG system can be adapted to fit particular use cases [2]

RAG Architectures - Naive RAG is contrasted with Agentic RAG, highlighting architectural differences [1] - The explanation includes visuals to aid understanding of the two RAG approaches [1] Key Concepts - The document clearly explains the concepts of Naive RAG and Agentic RAG [1]

Key Features - LMCache is an open-source LLM serving engine designed to reduce time-to-first-token and increase throughput [1] - LMCache particularly excels in long-context scenarios [1] - LMCache boosts vLLM with 7x faster access to 100x more KV caches [1] Open Source - LMCache is 100% open-source [1]

LLM Serving Engine - LMCache is an open-source LLM serving engine designed to reduce time-to-first-token and increase throughput, especially under long-context scenarios [1] - LMCache boosts vLLM with 7x faster access to 100x more KV caches [1] Open Source - LMCache is 100% open-source [1]

GitHub repo: https://t.co/f9vvUucTne ...

Key Features - LMCache is an open-source LLM serving engine designed to reduce time-to-first-token and increase throughput [1] - LMCache significantly improves vLLM, achieving 7x faster access to 100x more KV caches [1] Technical Advantages - The solution is particularly beneficial in long-context scenarios [1]

RT Avi Chawla (@_avichawla)How LLMs work, clearly explained (with visuals): ...

That's a wrap!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.Avi Chawla (@_avichawla):How LLMs work, clearly explained (with visuals): ...