Category: RAG

RAG at Scale: Architecture Patterns for Enterprise-Grade Retrieval

Basic retrieval-augmented generation works well in demos and poorly in production. The demo shows a clean pipeline: chunk text, embed chunks, retrieve relevant chunks, feed them to the model. The prod

Case Study: End-to-End RAG Platform for Customer Support

A SaaS company with 200 support agents and 10,000+ knowledge base articles had an 18-hour average response time and 23% first-contact resolution. Their largest enterprise client threatened to cancel a

Retrieval-Augmented Generation at Scale: Designing the RAG Pipeline

Large language models suffer from a critical flaw: their knowledge is frozen at training time, encoded implicitly in billions of parameters, and prone to confident fabrication. This limitation becomes