TRAIL
Shipping reliable AI
From framing the problem to a model choice you can defend, with an evaluation set that actually catches regressions.
Most AI feature failures trace back to a decision made before any code was written: the wrong frame, the wrong model for the constraint, or no real evaluation behind a change that shipped anyway. This trail is the front half of building something reliable.
By the end you'll be able to frame a problem before reaching for a model, choose a model against real constraints instead of a leaderboard, constrain output to a shape your code can trust, and build an evaluation set that catches a regression before your users do.
Begin trail →- 1BuildFrame the problem before the prompt
Before any architecture: what decision is this system making, what does wrong cost, and who checks it. Everything downstream should answer to this.
- 2BuildChoosing a model
Not "which model is smartest" but which one fits your task, your latency budget, and your cost at real volume.
- 3BuildStructured output
Free text is fine for a chat window. A pipeline needs a shape it can parse without guessing — and validation on top of that shape.
- 4BuildRetrieval design
If the system is grounded in documents, retrieval quality — chunking, ranking, freshness — decides the answer long before the model does.
- 5BuildBuilding evaluation sets
Replace "it looked better when I tried it" with a fixed, versioned test set you re-run on every change.
- 6BuildTesting AI systems
Beyond correctness: does it fail predictably when it doesn't know, and hold up against an adversarial or paraphrased input.