Rag

The VP of Product walks into the daily standup: “I want the chatbot to answer questions about our internal documentation. We have 2,000 pages of runbooks, policies, and procedures. ChatGPT doesn’t know any of that.” The ML team says: “We’ll implement RAG.” Everyone nods. You get the job of provisioning the infrastructure. Before you start creating resources, it’s worth understanding what RAG is actually doing under the hood. The map for infra engineers RAG concept What it does Infra equivalent Retrieval Finds relevant documents Search engine query Augmentation Adds docs to the LLM prompt Build the request payload Generation LLM produces an answer using the context The model response Chunking Splits documents into smaller pieces Data partitioning, sharding Indexing pipeline Processes docs and generates embeddings ETL/data pipeline Hybrid search Combines semantic search + keyword search Using CDN + origin server together The problem RAG solves LLMs have two fundamental limitations: ...