This episode covers reasoning models, the shift from manually guiding a model's thinking to letting the model reason through complex problems on its own before responding. It explains the concept of test-time compute, why reasoning models take longer but perform dramatically better on hard tasks, and how they change the way you should prompt. It walks through when to reach for a reasoning model versus a standard one, and closes by framing the full prompt engineering toolkit in context, from few-shot examples through reasoning models.

This episode covers structured output, how you get a model to respond in predictable, machine-readable formats like JSON instead of natural language paragraphs. It walks through three approaches, from simply asking in the prompt, to JSON mode, to schema-based constraints, and explains why each level adds more reliability. It uses real-world examples to show how structured output turns AI from a conversation partner into a software component that can feed databases, trigger workflows, and drive automation. It closes with practical tips for writing schemas and validating output in production.

This episode covers system prompts, the invisible instruction layer that shapes every model interaction before the user says a word. It explains the three-role message format, why the model is trained to treat system instructions as higher authority than user messages, and how persona prompting works by shifting which region of the training distribution the model samples from. It walks through the anatomy of a good system prompt and closes with what happens when system and user instructions conflict, including a preview of the prompt injection problem.