Week 3: Talking to Models Properly
LLM foundations, structured outputs, prompt architecture, and secure API usage.
What the machine expects from you.
This lesson defines the operational shape of an LLM feature: request assembly, model invocation, output validation, persistence, and observability.
Teams fail when they treat the model call as the product. In reality, the product lives in the orchestration around the model: retries, output checks, storage, user messaging, and fallback behavior.
An LLM API is an unreliable-but-useful subsystem. Design around it the way you would around any external dependency: narrow interface, explicit validation, good telemetry, graceful degradation.
This lesson reframes prompting as interface design. You are not “talking nicely” to the model; you are constraining a probabilistic component into a usable contract.
Three dense lessons, one enforced deliverable.
How LLM APIs Fit Into Real Products
Models are one service in a larger system, not the whole app.
LessonPrompting as Interface Design
Good prompts define boundaries, expected outputs, and decision rules.
LessonPrompt Injection, Secrets, and AI Transparency
Every LLM feature is also a security and trust problem.
What survives the week.
Prompt Contract
A production-oriented contract for role, input, constraints, output shape, and validation expectations.
A prompt contract and structured-output integration design.
Each week leaves behind portfolio evidence that compounds into the final SaaS and its operating narrative.