@ingliguori

This is one of the cleanest visual summaries of a production-grade RAG (Retrieval-Augmented Generation) stack I’ve seen. What it highlights clearly is an often-ignored reality: RAG is not a single tool — it’s an ecosystem. A solid RAG system spans multiple, interchangeable layers: LLMs (open & closed): Llama, Mistral, Qwen, DeepSeek, OpenAI, Claude, Gemini Frameworks: LangChain, LlamaIndex, Haystack — orchestration is the real differentiator Vector databases: Chroma, Pinecone, Qdrant, Weaviate, Milvus Data extraction: Web crawling, document parsing, structured ingestion Embeddings: Open (BGE, SBERT, Nomic) vs proprietary (OpenAI, Cohere, Google) Evaluation: RAGAS, TruLens, Giskard — because “it sounds right” is not a metric Key takeaway for leaders and builders: RAG success is less about which model you choose and more about: data quality retrieval strategy chunking & indexing evaluation loops cost / latency trade-offs This is why mature AI teams design modular stacks, not one-vendor pipelines. RAG is no longer experimental. It’s becoming foundational infrastructure for enterprise AI. #RAG #AgenticAI #EnterpriseAI #LLMs #AIArchitecture #GenAI #DataEngineering X (Twitter) RAG isn’t a tool. It’s a stack. LLMs Frameworks Vector DBs Embeddings Extraction Evaluation Winning teams design modular RAG systems — not single-vendor pipelines. This is how enterprise AI actually scales.

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