rag

The fine-tuning versus retrieval framing hides several options that are often better than either. This lesson covers long context and why it does not replace retrieval, prompt caching as a cost lever, agentic retrieval, continued pretraining, and how to revisit a decision that was right when you made it.

The decision is only half the work. This lesson covers what building each actually involves: parameter-efficient fine-tuning with LoRA and why it made the technique accessible, the training data problem that stalls most projects, the retrieval pipeline end to end, and how to combine them into one system.

Fine-tuning and retrieval are usually presented as alternatives when they change different things. This lesson separates them precisely: fine-tuning adjusts weights and teaches behaviour, retrieval supplies context and teaches nothing, and the question that decides between them is whether your problem is knowledge or form.

Dense vectors miss exact terms, so production retrieval combines them with lexical search. This lesson covers hybrid retrieval and reciprocal rank fusion, a tuning method that starts from a stated recall target, capacity planning, the operational failures that catch teams, and how to choose between running your own index and buying a service.

Vector search exists because exact nearest neighbour search is intractable at scale and the curse of dimensionality defeats the classical index structures. This lesson covers distance metrics and when each is right, why brute force costs what it does, why k-d trees fail above a few dozen dimensions, and the recall-latency trade that every approximate index makes.

Since a model cannot judge its own output, detection has to compare it against something external. This lesson covers grounding through retrieval and why it reduces rather than eliminates the problem, then the detection methods that work: self-consistency sampling, entailment checking against sources, claim decomposition, and chain-of-verification.

The hard parts of an internal knowledge system are not the ones a public RAG tutorial covers. This lesson builds the architecture: connectors and the ingestion path, the permission problem and why early binding beats late binding, oversharing inherited from your existing access control, entity resolution across silos, and the staleness and conflicting-truth problems that break internal corpora.

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.