@legitalgorithmswithpeter: LLM vs RAG — what’s ACTUALLY the difference? 🤖🧠 First, RAG isn’t an alternative to an LLM. 🧠 LLM = The model that understands and generates language 📚 RAG = An architecture that gives the LLM access to external knowledge An LLM stores learned knowledge in its model weights. But it doesn’t automatically know your company’s private documents, latest database records, or a PDF created five minutes ago. 👀 RAG solves this by retrieving relevant information at query time: ❓ User asks a question 🔢 Query becomes an embedding 🔎 Relevant data is retrieved 📄 Context is added to the prompt 🤖 LLM generates the answer And production RAG is much more than “put documents in a vector database.” It can involve chunking, embeddings, filtering, reranking, retrieval, context construction, and evaluation. ⚡ The biggest difference: 🧠 LLM → Knowledge learned during training 📚 RAG → External knowledge retrieved at runtime So which is better? It’s usually not LLM vs RAG. The LLM is the brain. 🧠 RAG gives that brain access to external knowledge. 📚 Would you add RAG to every AI application? 👇 #RAGArchitecture #LLMApplications #RetrievalAugmentedGeneration #VectorDatabase #AIEngineering

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Thursday 27 August 2026 23:06:25 GMT