A complete walkthrough of retrieval-augmented generation: chunking, embeddings, retrieval, and evaluation.

Fixed-size, semantic, and hierarchical chunking — how chunking quality drives RAG answer accuracy.

Embeddings, similarity metrics, and approximate nearest neighbor search explained step by step.

Prompt design, function calling, guardrails, and cost control for production LLM integrations.

How AI agents differ from deterministic automation and when each approach makes sense.

The security risks of LLM-powered systems and the guardrails that reduce them.

Retrieval metrics, relevance judgment, and evaluation harnesses for measuring RAG quality.

Grounding, retrieval, temperature, and self-consistency — practical techniques to reduce hallucinations.

Dedicated vector stores vs. extensions, and the criteria for picking the right one for your workload.