Business challenge

Move beyond a fragile AI prototype

Replace a convincing happy path with repeatable evaluations, defined limits and recovery when things go wrong.

The useful questions.

  • What changes between the demo and real use?
  • Which mistakes are unacceptable?
  • How will model or data changes be checked?

Where we would start.

  • Build a representative evaluation set
  • Separate retrieval, generation and action failures
  • Add operating limits, review and monitoring

Connected examples

Explore a relevant possibility.

Fictional scenarios that make the engineering easier to explore.

The next possibility

What will you build next?

Tell us where you want to go. We’ll help define the next step.

Explore with AI