You ask your RAG system: ‘What are the cross-cutting themes and operational risks across 500 enterprise audit reports?’. Your vector database fails because it only understands word proximity, not relationships. GraphRAG merges knowledge graphs, hierarchical community detection, and LLMs to transform unstructured data into a navigable cognitive map.
During the RAG gold rush, hundreds of engineering teams signed up for $300/month dedicated vector databases to index a few thousand documents. In real production, PostgreSQL with pgvector handles that exact search in 8 milliseconds, at zero additional cost, while preserving full ACID integrity. This is the definitive architecture and performance breakdown.
Your AI model hallucinates with your company’s data. Is the fix RAG, fine-tuning, or a better prompt? The right answer depends on a single question almost nobody asks. After implementing all four techniques in production, I’ve distilled the decision tree I wish I’d had when I started.
RAG is the aspirin of generative AI: everyone prescribes it, almost no one truly understands how it works, and when it fails, the patient hallucinates. After implementing RAG in production with the Ops Copilot and discarding it in favor of Tool Calling for the obsolescence radar, I’ve catalogued the 7 errors that destroy precision — and the solutions that actually work.
When visitors have questions about my 72 blog posts, they shouldn’t have to dig through archives. I built an AI copilot that uses Algolia Agent Studio to deliver conversational answers backed by real content.