From RAG POC to Production: Making Stronger Retrieval Practical
In the first post, I covered the initial architecture for building a production Django RAG chatbot without turning it into an expensive science project. The first version used a straightforward retrieval path: embed the user’s question, search PostgreSQL with pgvector, pass the retrieved context to the LLM, and log enough…




