Stanley's AI Systems Lab

AI Product Engineering

This track is for engineers who need AI features to behave like product surfaces, not experiments hidden behind a chat box.

Search intent

Readers want implementation patterns for production AI: how to choose interfaces, constrain outputs, handle bad inputs, and measure quality over time.

Who this is for

Full-stack engineers, applied AI builders, and product teams adding model-backed features to existing software.

Outcome

Ship AI product workflows with explicit contracts, source handling, failure states, and regression checks.

What you will build

A model interface decision tree covering chat, structured output, retrieval, and tool calling.

A prompt contract that names inputs, output schema, refusals, and review rules.

A deterministic fallback path for latency, cost, low confidence, or missing context.

An eval harness that tracks quality, cost, latency, and recurring reviewer edits.

Curriculum

Pick the right model interface

Choose between chat, JSON output, retrieval, and tools based on the product job.

Compare model behavior with real prompts

Use side-by-side prompt tests before committing to a model or prompt contract.

Design for the failure path

Plan low-confidence states, timeouts, missing context, and human review before launch.

Proof payloads to create as you learn

The goal is not to read more AI takes. Build reusable artifacts that make the workflow inspectable, reviewable, and credible.

Prompt contract template with version, inputs, schema, and reviewer notes.
Eval fixture set with real examples and expected outputs.
Failure-state copy and fallback decision table.

Get the next AI Product Engineering tutorial

Practical, proof-backed AI systems notes for founders and product engineers. No prompt-hack filler.