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Turn a clinical failure into a testable change.
For teams whose AI overlooks clinical context, gives inconsistent guidance or responds differently after an instruction or model change.
When this is useful
You have a specific failure to investigate or a clear behaviour to improve. A useful project starts with the intended use and evidence of the problem, then examines instructions, context and workflow together.
How the work runs
- Investigate possible contributors without assuming the prompt is the only cause.
- Propose or implement a limited agreed instruction or safeguard change with your team.
- Where commissioned, compare the original and revised behaviour on anchor cases and new edge cases.
What your team receives
- A documented failure hypothesis and targeted change proposal.
- Revised instructions or workflow requirements within the agreed scope.
- Where included, before-and-after observations, remaining failures and next tests.
Scope and limits
Prompt changes can introduce new problems. Improvement is assessed through agreed tests and is not guaranteed. Software integration, model training and extensive retrieval engineering are scoped separately or referred to suitable specialists.
Start with your clinical AI question.
Send a short, non-confidential overview. I'll assess fit and discuss a written scope, fee and timing.