By Naveen Ayalla This article is adapted from my original post in the Databricks Community and is...
By Naveen Ayalla
This article is adapted from my original post in the Databricks Community and is shared here for developers, data engineers, and GenAI practitioners building production AI workflows.
A RAG demo is easy to build compared to a production RAG system.
For a demo, you can upload documents, create embeddings, connect an LLM, ask a question, and return an answer.
That is a great starting point.
Build a RAG pipeline from scratch: chunking, embeddings, retrieval, reranking, grounded generation, and the production patterns…
RAG is not new. Chunk a document, embed the chunks, store them in a vector database, run a retrieval...
A practical RAG evaluation checklist for AI SaaS builders to test retrieval quality, grounded answers, citations, regressions,…
Your RAG demo works on localhost. Under real load—socket exhaustion, 429s, and pool timeouts kill it. A frontend dev's…
Most "chat with your documents" demos work in an afternoon. Then you hit the last 20%: retrieval that...
Building a basic Retrieval-Augmented Generation (RAG) prototype is a weekend project. You pip install...
