Built a RAG app from scratch and learned more than any tutorial could teach me. Stack: • LangChain for orchestration • Redis (async) for blazing-fast conversational memory • Pinecone as the vector database for scalable semantic search What clicked for me 👇 RAG isn’t about “adding documents to an LLM”. It’s about systems thinking. Ingestion → chunking → embeddings → retrieval → reranking → grounding → response. Redis async solved a real pain point: state + speed without blocking the app. Pinecone handled scale without me worrying about infra. The biggest lesson: A good RAG system fails gracefully. A bad one hallucinates confidently. This project forced me to think like an engineer, not just a prompt writer. Next steps: • better retrieval evaluation • hybrid search • tighter grounding • production-grade monitoring If you’re building with LangChain, RAG, or vector DBs, let’s connect. Still early, but building in public 🚧💡 Onward. github - https://t.co/eC60ZWYKoc #RAG #LangChain #Redis #Pinecone #LLM #GenAI #AIEngineering #BuildInPublic

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