Generative AI
Factual, plain-language explainers on the generative-AI building blocks you'll meet when shipping AI-native software — retrieval-augmented generation, embeddings, fine-tuning versus prompting, agents and tool use, structured outputs, and how to evaluate and cost it all. Each concept is written to be read the moment it shows up in your work: what it is, why it exists, what you'll see in your code, and where it goes wrong.
Evaluating LLM Outputs
How to build evaluation systems for LLM-powered features — covering human eval, automated checks, LLM-as-judge, eval datasets, and regression prevention.
Structured Outputs
Structured outputs are techniques for getting LLMs to reliably produce machine-parseable data like JSON — essential for any pipeline that needs to process model responses programmatically.
RAG — Retrieval-Augmented Generation
RAG gives an LLM access to specific knowledge at query time by retrieving relevant documents and passing them as context — without retraining the model.