ost product teams writing requirements for AI features adapt a standard PRD template — adding a section labelled "data" and calling it done. This produces requirements documents that fail in three consistent ways: they do not define failure behaviour, they do not include monitoring requirements, and they treat model performance as an engineering concern rather than a product decision.
The result is that the "AI PRD" is really just a feature PRD with a vague data section. The engineering team interprets the ambiguity as license to make product decisions — confidence thresholds, fallback behaviour, edge case handling — in code. Decisions made in code are invisible to product and business stakeholders until they surface as user complaints or compliance findings.
**What Makes AI Requirements Different**
Standard software features are deterministic: given input X, the system always produces output Y. An AI feature is probabilistic: given input X, the system produces output Y with some confidence — and the confidence varies by context, data quality, and time.
This fundamental difference means your PRD needs six sections that a standard template does not have.
**Section 1: Data Requirements**
Not "we need customer data" — but: which specific fields, from which source systems, updated how frequently, with what minimum completeness rate, in what format, and with what labelling methodology if supervised learning is involved.
The data requirements section should be specific enough that a data engineer can immediately assess feasibility without a follow-up conversation. Include known data quality issues. If a field has a 15% null rate and you are depending on it for a core model feature, document this — and include a mitigation (imputation strategy, exclusion criteria, or a data quality improvement prerequisite).
**Section 2: Model Constraints**
What the model is explicitly not allowed to do. This is as important as what it should do — and it is the section most likely to prevent a compliance incident post-launch.
Constraints include: prohibited input attributes (e.g., no protected class attributes may be used as model inputs), latency requirements (e.g., inference must complete in under 200ms for real-time use cases), explainability requirements (e.g., model outputs must be interpretable by a non-technical auditor), output format requirements (e.g., outputs must include a confidence score), and geographic or demographic scope limitations.
**Section 3: Confidence Thresholds and Fallback Behaviour**
At what confidence level should the AI output be shown to the user? What happens when the model's confidence falls below that threshold — is the output suppressed, flagged for human review, or shown with a caveat?
This is a product decision, not an engineering decision. Writing it in the PRD forces the product team to answer the question before engineering builds the wrong behaviour into the system.
Example: "If the document classification model's confidence score is below 0.80, the document is routed to the manual review queue rather than auto-classified. The user interface displays 'Under Review' rather than showing a low-confidence classification."
**Section 4: Edge Case Register**
Document at least 10 known edge cases — inputs that are unusual, ambiguous, or likely to produce incorrect model outputs — and specify the required system behaviour for each.
This section is skipped most often because edge cases are uncomfortable to think about before a model exists. But a shipped product without a documented edge case register has accepted unknown behaviour in unknown scenarios. When those scenarios occur in production — and they will — there is no specification to determine whether the behaviour is a bug or a design decision.
**Section 5: Monitoring Requirements**
Define what gets measured post-launch, how frequently, and what thresholds trigger an alert or an incident.
At minimum, AI products require: model accuracy against a holdout dataset (reviewed monthly), feature drift detection (weekly comparison of live input distributions against training data), and a business outcome metric directly linked to the D1 problem statement (reviewed monthly).
Also define the performance floor: if accuracy drops below X%, an incident is raised and a rollback procedure is initiated. The rollback procedure itself should be documented — what does reverting to the pre-AI behaviour look like, and who has the authority to trigger it?
**Section 6: What This Feature Does NOT Do**
This is the most underrated section in any AI PRD. Explicitly document the scope boundaries — what the AI feature is not responsible for, what data it does not use, what decisions it does not make.
This section prevents two failure modes: scope creep in user expectations (users believe the AI does more than it does) and blame attribution (when a related failure occurs, someone assumes the AI caused it). Both are easier to prevent in a PRD than to manage after launch.
Write this section as a bulleted list of explicit exclusions: "This model does not predict X," "This feature does not process Y data type," "This recommendation engine does not account for Z."
**Putting It Together**
The six sections above are the Define stage (D3) of the Business-First AI™ methodology. They are not additions to a standard PRD — they are the AI-specific requirements framework that replaces the generic data section most teams write and ignore.
Download the free PRD Template for AI Products from the Business-First AI™ Framework Library for a ready-to-use template covering all six sections.