How Designers Should Work When AI Joins Requirement Analysis

1. The requirement document describes a solution, designers must see the underlying problem

Requirement docs often list what the team wants to build, not the real user problem. A sample statement – “Users face too much content, we need an automatic summary feature” – mixes facts, goals, assumptions, and a proposed solution. Without unpacking these layers, designers may treat an unverified solution as the requirement itself, risking work on the wrong problem.

Designers should treat the requirement as an iceberg: the visible surface contains product background, logic, goals, and feature description, while beneath lie user scenarios, genuine needs, constraints, risks, and justification evidence that must be validated before proceeding.

2. Why AI often gives “seemingly reasonable” analysis

Four common shortcuts cause AI to appear trustworthy while hiding flaws:

Forcing unrelated matches – AI links unrelated content simply because of keyword similarity, without confirming relevance or conditions.

Skipping reasoning – AI may output conclusions and reasons without indicating which requirement element each reason stems from.

Re‑phrasing original text as a quote – AI summarizes knowledge‑base entries and adds quotation marks, making it look like a direct citation.

Hallucinating facts – When source material is missing, AI fills gaps with commonsense or guessed information, mixing fact, experience, and model speculation. OpenAI research notes that rewarding only accuracy can encourage such guessing.

These shortcuts make errors harder to detect because the process is hidden.

3. How to make AI’s judgment and analysis more reliable

The skill is reorganised into three parts:

Collect judgment criteria : Identify what knowledge‑base items the AI should consult, and annotate each with its premise, applicability, and link back to the original source.

Define mandatory analysis steps : (1) Split facts, goals, assumptions, and proposals; (2) Find supporting evidence and verify its conditions; (3) Explain the relationship between requirement data and evidence; (4) Surface conflicts, risks, and information gaps; (5) Produce a conclusion and next‑step recommendations.

Perform final checks : Verify that each evidence item truly matches the requirement, that its applicability is justified, that citations can be traced, and that any unsupported parts are marked as unknown. Run the skill against historical cases and deliberately crafted error examples to ensure the rules work.

4. What a good requirement analysis should show the team

A robust analysis surfaces four key pieces of information:

What we already know : Confirmed facts such as user feedback, scenarios, existing flows, and constraints.

What we are assuming : Unverified hypotheses that need validation before they become facts.

Why we reached the current judgment : Explicit links between design principles or historical experience and the specific requirement, including applicability conditions.

What needs to be confirmed next : Who to consult, what evidence to gather, which assumptions carry the highest risk, and which decisions should be postponed.

When uncertainty is clearly expressed, “unknown” becomes a manageable risk rather than a hidden flaw.

5. Designers actually build a learning loop

The skill is continuously iterated: start with a small, concrete task, observe AI’s output, record deviations, codify effective rules, and re‑evaluate with new or old cases. This mirrors common AI‑engineering practice of beginning with simple solutions, evaluating them, and only adding complexity when necessary.

Each failure is turned into an asset – a problem record, a failing example, a template, or a checklist – so future experiments start from existing knowledge instead of zero.

6. Conclusion: AI does not replace designers’ judgment, it clarifies the process

The skill’s purpose is to encapsulate stable steps of requirement analysis so AI can consistently decompose information, invoke evidence, and perform checks. The deeper purpose of analysis is to help designers see the problem, not to hand over decision‑making to the model.

Even as AI gets better at generating content, designers remain responsible for interpreting context, weighing values, and owning the final choices.

References

Apple, Human Interface Guidelines, https://developer.apple.com/design/human-interface-guidelines/

OpenAI, Why language models hallucinate, 2025‑09‑05, https://openai.com/index/why-language-models-hallucinate/

OpenAI, Working with evals, https://developers.openai.com/api/docs/guides/evals

Anthropic, Building Effective AI Agents, 2024‑12‑19, https://www.anthropic.com/engineering/building-effective-agents