Context / Tools / Evals / Safety / 01
Find the right product seam
Choose work where ambiguity is useful, review is possible, and the model improves an existing outcome.
Before you begin
By the end, you can…
- Identify tasks that tolerate uncertainty
- Define human review and failure cost
- Reject weak AI use cases
01 / Understand
Start with the workflow, not the model
AI is useful where interpretation, generation, classification, or transformation creates value and a person or system can evaluate the result. It is weaker where exactness is mandatory and errors are hard to detect.
Map the existing workflow, frequency, pain, available context, review point, and cost of failure. Compare the AI concept against a simpler search, rule, template, or interface improvement.
02 / Apply
Write a value and risk contract
Define the user outcome, baseline, success measure, unacceptable failure, escalation path, and authority boundary. The model should not quietly gain more power than the user intended.
Prototype the workflow with human-generated outputs before integrating a model. If the interaction is not useful with good outputs, better model quality will not rescue it.
03 / Make
Your studio task
Make — Evaluate three candidate AI features and reject at least one.
- List three candidate AI features in an existing workflow.
- Score value, uncertainty tolerance, reviewability, and failure cost.
- Compare the strongest idea with a non-AI alternative.
- Prototype the workflow and reject at least one candidate explicitly.
The selected use case tolerates uncertainty, supports review, and improves a measurable outcome.
04 / Check