AI for Builders

Grounding & Guardrails

LLMs are confident about everything — including things they've made up. This series covers how to ground model outputs in real data using RAG, how to improve retrieval quality, and how to build the safety guardrails your product needs to ship.

4 parts · ~35 min total

  1. Grounding Models in Your DataThe RAG pipeline from first principles — embeddings, vector search, and how to give an LLM access to your company's knowledge.~9 min3 interactive demos
  2. When RAG Isn't EnoughChunking strategies, hybrid search, reranking, and query rewriting — the optimization layer that turns a basic RAG system into a reliable one.~8 min3 interactive demos
  3. Hallucinations, Safety & TrustWhy models confabulate, how to detect it, content filtering, red-teaming, and the trust architectures that let you ship AI responsibly.~8 min3 interactive demos
  4. Graph RAG & Structured RetrievalWhen vector search can't follow relationships — knowledge graphs, text-to-SQL, metadata filtering, and the retrieval router pattern that combines them all.~10 min2 interactive demos