Prompt Engineering Is Requirements Engineering

Why does AI fail? Because: “Most problems simply aren’t defined well enough at the start, so you don’t have the information you need to build the right solution.“

It’s the classic “do what I meant, not what I said” problem! The matching solution is requirements engineering: “identifying, analyzing, documenting, and managing the needs of stakeholders for a product or system.”

Requirement engineering is a skill that is not replaced by AI but becoming even more important: “Prompt engineering and requirements engineering are literally the same skill—using clarity, context, and intentionality to communicate your intent and ensure what gets built matches what you actually need.”

“Prompting failures can be traced to problems with the process, not the people. They typically stem from poor context and communication, not from ‘bad AI.'”

Therefore context engineering is so important: “deciding what the model needs to see to generate something useful, which typically includes surrounding code, test inputs, expected outputs, design constraints, and other important project information.”

Read more about requirements, prompt and context engineering:

👉 https://www.oreilly.com/radar/prompt-engineering-is-requirements-engineering/

However, engineering is only the second step. The first step is design and, accordingly, context design. At Datentreiber, we use the Miro template for a Multi-Agent System (MAS) Design Workshop

👉 https://miro.com/templates/multiagent-system-mas-design-workshop/