rag

Learn how to systematically evaluate Retrieval-Augmented Generation systems in production using RAGAS, TruLens, and Phoenix — covering golden sets, retrieval drift, embedding drift, and cost-aware eval scheduling.

How to manage, compress, and strategically fill the context window in long-horizon agents — covering summarization checkpoints, scratchpad memory, retrieval injection, prompt caching, and compaction triggers.

A rigorous guide to measuring RAG quality using RAGAS metrics — faithfulness, answer relevancy, context precision, and context recall — plus how to build a golden dataset and recognize where automated metrics fall short.

Go beyond naive RAG pipelines. Learn how Self-RAG, Corrective RAG, and retrieval-as-tool patterns let an LLM decide when, what, and how many times to retrieve — enabling reliable multi-hop reasoning over complex knowledge bases.

Deep dive into two advanced RAG architectures — HippoRAG's hippocampal-inspired knowledge graph indexing and RAPTOR's recursive summarization tree — and why both dramatically outperform flat vector retrieval on multi-hop questions.

Go beyond dense-vector search: learn how Microsoft GraphRAG extracts entities, builds a knowledge graph, clusters it with the Leiden algorithm, and serves both local and global queries with community summaries — delivering answers that classic RAG cannot.

Master four advanced query rewriting techniques that dramatically improve RAG retrieval quality: Hypothetical Document Embeddings, multi-query expansion, query decomposition, and step-back prompting. Learn when to reach for each and how to implement them.

A deep dive into how you split documents for retrieval-augmented generation — fixed-size, recursive, semantic, hierarchical, and late chunking — with concrete trade-offs and code for each approach.

The model is rarely the bottleneck. This lesson examines why data readiness — quality, governance, lineage, and access — is the primary constraint on enterprise AI value, with a practical scorecard, and a clear-eyed comparison of RAG versus fine-tuning economics.

Dive into the world of vector databases, specialized systems designed to store and query high-dimensional vector embeddings efficiently. Learn how these databases power semantic search, recommendation systems, and large language model applications by finding semantically similar data points at scale.