Recently, the AI4RE micro-credential was introduced by International Requirements Engineering Board (IREB). At first glance, it may look like just another certification topic around artificial intelligence. When you look closer, however, it becomes clear that AI4RE fits extremely well with the AI Unified Process (AIUP).

This is not because both talk about AI, but because both put requirements and specifications back at the center of software development, exactly where they belong.

What AI4RE Is Really About

AI4RE is a micro-credential within the CPRE ecosystem that focuses on the practical and responsible use of AI in Requirements Engineering. The real value is not the badge itself, but the mindset it teaches.

AI4RE helps practitioners understand what AI and large language models can realistically do, where their limits are, and how they should be used in daily RE work. It shows how AI can support elicitation, refinement, documentation, and validation of requirements, while at the same time emphasizing that AI output must always be reviewed critically. Topics such as ambiguity, bias, confidentiality, and trust in AI results are treated as first-class concerns, not as afterthoughts.

The goal is not to replace requirements engineers, but to make them more effective and more confident when working with AI.

A Short Recap: What AIUP Is

AIUP is not a tool and not a promise that AI will magically solve software development. It is a process that describes how AI can be used in a structured and disciplined way throughout the lifecycle of a system.

At the heart of AIUP are specifications. Requirements capture intent, system use cases describe observable behavior, and these artifacts drive code and test generation with the help of AI. Humans stay in control by reviewing the results, correcting mistakes, and refining both specs and code. The process is iterative and incremental, moving forward one system use case at a time.

An important point that is often misunderstood is that AIUP is not Big Upfront Design. You do not define everything in advance. Instead, you grow the system step by step, but always based on explicit and reviewed specifications.

Where AI4RE and AIUP Meet

AI4RE focuses on how to use AI in requirements work. AIUP focuses on how specifications drive the entire development lifecycle, including implementation and tests. This makes AI4RE a natural and very strong complement to AIUP.

When requirements and use cases are clearer and more precise, AI produces better results. When AI output is reviewed with the right mindset, fewer errors slip into the system. AI4RE strengthens exactly this part of the process, which directly improves the effectiveness of AIUP.

You can see AI4RE as reinforcing the front end of AIUP. Better specifications lead to better generated code, less rework, and faster learning cycles.

Requirements First, Not Code First

A key message in both AI4RE and AIUP is that the truth of a system is not hidden in the code. Code is an implementation detail. It changes over time and can be regenerated or updated. What must remain stable is the intent behind the system.

AI4RE teaches how to express this intent clearly, even when AI is involved in writing or refining requirements. AIUP ensures that this intent is not lost once implementation starts, because the specifications remain the primary driver.

This is especially important in modernization projects, where code exists but specifications are missing or outdated. In AIUP, system use cases can be derived from existing systems. AI can support this reverse engineering, but human review is essential. AI4RE provides the skills to do this safely and critically.

Prompt Engineering Is Not Enough

Many teams believe that better prompt engineering is the solution to all problems when working with AI. Both AI4RE and AIUP take a more realistic view.

In AIUP, prompts are not written in isolation. They are guided by concrete artifacts such as requirements, entity models, system use cases, and constraints. AI is not asked to guess what the system should do. It is asked to implement or update clearly described behavior.

AI4RE helps people understand how to evaluate AI responses and recognize when a prompt or a requirement is unclear. AIUP provides the structure that prevents prompt chaos by anchoring all AI interaction in shared and versioned specifications.

Responsible AI Is Built In, Not Added Later

Responsible use of AI is not an optional add-on in either approach. AI4RE explicitly addresses risks, limitations, and ethical concerns. AIUP translates this awareness into concrete working practices.

In AIUP, AI is never the final authority. Generated code and tests are reviewed. Version control is mandatory so changes can be tracked and reverted. Small, incremental updates are preferred over full regeneration, because today’s AI is not a compiler and does not produce identical results every time.

AI4RE explains why this level of care is necessary. AIUP shows how to apply it in everyday project work.

How AI4RE Fits into AIUP in Practice

In practice, AI4RE can serve as a foundation for teams working with AIUP. It can be used to align expectations and skills before AI is heavily used for specification refinement or code generation.

The concepts from AI4RE help teams write better system use cases, detect gaps and inconsistencies earlier, and validate behavior more effectively. In modernization projects, the combination of AI-assisted analysis and structured use cases makes it possible to regain control over complex systems step by step.

AI4RE provides competence. AIUP provides process. Together, they reduce randomness and increase confidence when using AI in real projects.

Not Competing, but Complementary

It is important to see AI4RE and AIUP as complementary, not as alternatives. AI4RE answers the question of how AI can be used responsibly and effectively in requirements engineering. AIUP answers the question of how a software project can be run when specifications are the central artifact and AI is used throughout the lifecycle.

Combined, they address a gap that many teams currently struggle with. AI is used everywhere, but often without structure, shared understanding, or clear responsibility.

Final Thoughts

AI is changing software development, but it does not remove the need for clear thinking and explicit intent. AI4RE helps practitioners use AI more effectively in requirements engineering. AIUP ensures that these requirements actually drive the system and do not get lost once coding starts.

This combination points in a healthy direction for the industry. Clear intent comes first, implementation follows, and AI acts as a powerful assistant rather than an oracle.

If you work with AI and still start with code, you are likely leaving a lot of value on the table.