A practical walkthrough for building semantic search with text embeddings, covering chunking, vector storage, cosine similarity, and the trade-offs that decide whether it works in production.

A practical walkthrough of retrieval-augmented generation: chunking, embedding, vector search, and answer synthesis, with working code and common failure modes.

A practical tutorial explaining how function calling (tool use) works, how to define tools, parse tool calls, and wire an LLM to a real external API, including failure modes and trade-offs.

A practical guide to how vector databases store embeddings, how similarity search works, and how to set up and query one for a real retrieval workflow.

A practical framework for scoring LLM outputs with explicit rubrics, pairwise comparisons, and automated checks, including runnable code and the trade-offs behind each method.

A practical tutorial on building LLM output guardrails: input classification, output filtering, layered checks, and the failure modes that catch first-time implementers off guard.

A step-by-step guide to building a working AI agent in Python using the OpenAI API, covering tool calling, the agent loop, and the failure modes that break first attempts.

A practical walkthrough of how function calling works in LLM APIs, with copy-pasteable examples, a full step-by-step implementation, and the trade-offs to weigh before shipping it.

A practical walkthrough of chunking for retrieval-augmented generation: why chunk boundaries decide retrieval quality, how to implement fixed, recursive, and semantic splitting, and how to verify the results.

A practical guide to the prompt structures and formatting choices that cause LLMs to return invalid JSON, with fixes, example prompts, and validation code.