Interactive constrained-generation product
A consumer app needed AI-generated content that was safe, repeatable, and followed strict domain rules. Open-ended generation produced too many failures. We built a constrained generation system with testable behaviour.
Context
An interactive generation workflow needed outputs that users could rely on—not one-off responses that varied unpredictably.
Constraint
Responses had to remain seconds-level for interactive UX while adhering to explicit domain rules that could not be violated.
Intervention
Added retrieval-augmented generation against a curated corpus. Enforced structured constraints around allowed outputs. Built end-to-end flows so behaviour could be tested in real user journeys.
Key decisions
- Retrieval-augmented generation
- Structured output constraints
- Domain rule enforcement layer
- End-to-end testable workflows
- Mobile and web delivery
- Feedback loop for continuous improvement
Outcomes
Generation failures dropped 80%. Maintained sub-3-second response time. Constraint violations fell to near-zero.
Why it matters
Constrained, testable AI behaviour makes features operable: teams can set limits, detect regressions, and ship iteratively.
Implementation
Practical technology choices that matched the constraints.
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