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.

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

Design AI workflows that keep humans in control where it matters and automate where it doesn't.

Establish benchmarks, test sets, and metrics so your AI systems can be measured and improved.