Prompting, Requirement Engineering, and the Quality of Outcomes

This is the first piece in Demystifying AI — a series that takes one AI concept at a time, breaks it down into plain language, explores its practical implications, and separates reality from hype. Given my background in banking and financial services technology, many examples will be drawn from that domain — though the concepts apply broadly.

One note before I begin: while the focus of this series is on AI concepts, any real-world adoption in regulated industries also has to account for security, privacy, governance, and compliance requirements. That's a constant backdrop worth keeping in mind, even in pieces like this one that are mostly conceptual.

Prompting feels surprisingly familiar

A prompt is often described as a question you ask an AI model. I think that framing undersells it. A prompt is closer to a requirement than a question — and if you've spent time in software delivery, that distinction should feel immediately familiar.

Consider a software project. If a business stakeholder says, "Build a fraud detection system," most experienced engineers won't start building. They'll ask:

• What types of fraud?
• Real-time or batch?
• What data sources?
• What level of false positives is acceptable?
• What compliance requirements apply?
• What are the expected outcomes?

Without that context, even the best engineering team will struggle to deliver the outcome the stakeholder actually wanted.

Now consider an AI prompt in the same light.

The model has to guess the audience, the depth, and the format — and it will guess.

The topic hasn't changed. But the audience, context, and expected outcome are now explicit — and the quality of the response improves because the quality of the requirement improved.

Effective prompting mirrors requirement engineering

In my view, the same principles that separate a good requirement from a vague one also separate a good prompt from a weak one:

• Clarity of objective
• Context
• Constraints
• Understanding the audience
• Expected outcomes

One of the more common misconceptions is treating AI like a traditional search engine. A search engine helps you find information that already exists. An AI system adapts its response based on context, audience, objective, and constraints — it isn't retrieving a fixed answer, it's constructing one for the specific requirement you gave it.

I'm curious whether this matches what others have seen in practice — has adding more context meaningfully improved the quality of the responses you get from AI tools?