Human–AI collaboration

When AI output needs constant correction, the difficulty may sit in the task definition, missing context or the way results are reviewed. We look at what you are trying to achieve before choosing a tool or writing a longer prompt.

A possible project could help a team prepare more consistent research briefs, use AI to explore options before a decision, or reduce the effort of reviewing generated drafts. The goal and the evidence needed to judge success come from your work, rather than a generic list of AI use cases.

We examine what a person knows that the system does not: the audience, constraints, relevant sources and examples of a good result. Prompt engineering gives those details a usable structure. Clear instructions also explain when the system should ask for clarification instead of filling a gap.

Then we decide how the output reaches the person using it. A draft should be easy to inspect and correct. An uncertain answer should invite verification. Approval points should match the decision being made, with enough context for the reviewer to exercise judgement.

Bring a recurring task, examples of current AI output, and an account of what takes too much effort. We can begin with focused advice or a prototype of a different way of working. Scope, deliverables and budget are agreed before the work begins.

We compare the proposed approach with representative tasks from the existing process. That can reveal whether clearer instructions are enough, whether the team needs better access to information, or whether a connected workflow is the next useful step. The result should fit how people actually work.