Approach Landing
RAG (Retrieval-Augmented) Use Cases
Architecture that grounds LLM responses by retrieving context from enterprise documents, databases and operational records.
Typical Stack
LLM (GPT-4o, Claude, Llama)Vector DB (Qdrant, Weaviate, pgvector)Embedding (text-embedding-3, BGE)Re-rankerOrchestration (LangChain, LlamaIndex)
Use cases using RAG (Retrieval-Augmented)
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