Most RAG setups look good in demos — until things get slightly complex. You ask a question, it retrieves “relevant” chunks, and everything seems fine. But as soon as your system spans multiple documents — APIs, billing, infra, workflows — things start breaking down. Not because the information isn’t there. RAG works by retrieving chunks based on similarity. That means: It finds text that looks relevant But doesn’t understand how pieces connect And can’t reconstruct system behavior So you end up with answers that are: technically correct but incomplete and often misleading In real systems: a deploy triggers a pipeline the pipeline applies changes to Kubernetes monitoring evaluates the rollout failures trigger rollback logic None of this lives in a single document. And RAG doesn’t connect these dots. I built Mindex: https://usemindex.dev/ Instead of just retrieving chunks, it builds a knowledge graph on top of your documents. So your AI can: connect documents follow relationships reconstruct flows Not just match text. Here’s a simplified comparison: Returns a flat list of documents No relationships No ordering No system understanding Connects documents Traverses relationships Infers flows (cause → effect) Provides structured context The difference is subtle at first. But when you're working with: internal documentation APIs distributed systems It becomes critical. You don’t just need relevant text. You need to understand how things work together. Mindex combines: semantic search a knowledge graph layer relationship traversal It’s available via: CLI MCP (works with tools like Claude Code, Cursor, etc.) REST API You can try it here: https://usemindex.dev/ I’m especially interested in feedback from people: building with RAG working with internal knowledge bases building AI dev tools Curious to hear how you're handling this today.