What is Prompt Engineering?

Prompt engineering is often dismissed as simply asking the AI the right question, but in practice it is closer to writing a detailed brief for a specialist contractor. The quality and specificity of your instructions directly determine the quality of the output, and in regulated industries the difference between a good prompt and a poor one can be the difference between a useful tool and a compliance liability.

A well-engineered prompt for a regulated context includes several elements beyond the basic question. It specifies the role the AI should adopt, the format of the expected output, the constraints it must operate within, and the sources it should draw from. For example, a prompt used to summarise client meeting notes for compliance records might specify that the summary must include all advice given, all risk warnings provided, the client outcome discussed, and must flag any statements that could not be verified from the transcript.

For mid-market firms, prompt engineering is the highest-return AI investment you can make. Before spending on custom models or complex infrastructure, optimising how your team interacts with existing AI tools typically delivers significant improvements. Firms that develop a library of tested, approved prompts for common tasks see more consistent outputs and faster adoption than those that leave each employee to experiment independently.

The governance aspect of prompt engineering matters in regulated industries. Prompts used for client-affecting processes should be version-controlled, tested against edge cases, and reviewed periodically. If a prompt is generating suitability report drafts, it should be treated with the same rigour as a document template. Changes should be logged, and the impact on output quality should be assessed.

Training your team in prompt engineering does not require technical backgrounds. The best prompt engineers are typically domain experts who understand what a good output looks like and can articulate their requirements clearly. This makes it an ideal starting point for AI enablement programmes.