Why the usual PRD fails AI features in 2026: what to add to the spec
The classic PRD with user stories and acceptance criteria no longer protects AI features: they often break at the stage of defining the task itself. LLM behavior depends on the prompt, the model, the evaluation data and a long tail of edge cases that an ordinary PRD does not cover. We look at which sections are worth adding to the spec so the team can measure quality and control risks after launch.
AI-processed from Habr AI; edited by Hamidun News
Product managers and AI product developers are running into a new problem: the familiar PRD with user stories and acceptance criteria stops working for LLM-based features. They start breaking down while the team is still trying to describe what exactly it plans to build.
Why the old PRD format doesn't fit
A classic software feature is deterministic: given the same input, the system produces the same output, and acceptance criteria can be written as a clear checklist. The behavior of an LLM-based AI feature depends on far more variables: how the prompt is worded, which model is chosen, the quality of the data used to evaluate results, and a range of edge cases that are hard to list in advance. A spec written in the old format simply does not cover these sources of uncertainty.
- The problem shows up when the task is being described, not only at release
- The classic PRD does not account for how a feature's behavior depends on the prompt and the model
- It also ignores the data used to evaluate quality and the edge cases of an LLM feature
- The proposal is to extend the spec with new sections that address these risks
What to add to the spec
The core idea is to extend the PRD with sections that describe not only the desired behavior of the feature, but also how the team will measure the quality of the model's answers, what data it will use for evaluation, and how it will respond to deviations after launch in production. This moves work that used to happen after an incident into the planning stage: the team defines up front which edge cases count as critical and how to track them.
What this means
The shift from deterministic features to LLM-based products changes the practice of product documentation itself: instead of fixed acceptance criteria, teams move to quality metrics, test datasets and risk-monitoring plans written directly into the spec. This reflects a broader shift in the industry: AI products need not just new features in the PRD, but a new document format altogether.
Key Questions Answered
Why the old PRD format doesn't fit?
A classic software feature is deterministic: given the same input, the system produces the same output, and acceptance criteria can be written as a clear checklist. The behavior of an LLM-based AI feature depends on far more variables: how the prompt is worded, which model is chosen, the quality of the data used to evaluate results, and a range of edge cases that are hard to list in advance. A spec written in the old format simply does not cover these sources of uncertainty. The problem shows up when the task is being described, not only at release The classic PRD does not
What to add to the spec?
The core idea is to extend the PRD with sections that describe not only the desired behavior of the feature, but also how the team will measure the quality of the model's answers, what data it will use for evaluation, and how it will respond to deviations after launch in production. This moves work that used to happen after an incident into the planning stage: the team defines up front which edge cases count as critical and how to track them.
What this means?
The shift from deterministic features to LLM-based products changes the practice of product documentation itself: instead of fixed acceptance criteria, teams move to quality metrics, test datasets and risk-monitoring plans written directly into the spec. This reflects a broader shift in the industry: AI products need not just new features in the PRD, but a new document format altogether.
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