#rag
3 articles
Design a GraphRAG System (Knowledge-Graph-Augmented Retrieval)
When vanilla vector RAG fails on "summarize the entire corpus" and multi-hop questions, you build a knowledge graph first — covering entity extraction, Leiden community detection, map-reduce global search, and graph traversal for multi-hop, based on Microsoft GraphRAG and production deployments at Neo4j, LinkedIn, and Writer.
Design a Customer-Support AI Assistant
Architect a production customer-support AI that deflects 60–80% of tickets by combining RAG over a help center, real-action tools (refunds, cancellations, account changes), per-session memory, guardrails, and a structured handoff to a human agent — all while keeping hallucination below 2%.
Design a RAG (Retrieval-Augmented Generation) Pipeline
Ground an LLM in 10 million documents (50 million chunks) with sub-2-second answers and a hallucination rate measurable by automated eval — the end-to-end ingestion, retrieval, reranking, and generation pipeline powering enterprise knowledge assistants.