AI Product Requirements Nobody Writes Down

TL;DR

• AI product specs define what the model should do but almost never define what the model should understand about the user, this missing section is why AI products feel generic
• User understanding requirements specify what the AI should know about each user, when it should learn it, and how that knowledge improves the experience
• Adding this section to your PRD is the single highest-leverage change because it forces the team to design for personalization from day one instead of bolting it on later

AI product requirements that nobody writes down are user understanding requirements: what the AI should know about each user, when it should learn it, and how that knowledge improves the experience. Every AI PRD specifies model behavior, but zero out of 40 reviewed documents defined what personalized actually means in measurable terms. This post covers the missing PRD section, the five questions user understanding requirements must answer, and a template that forces teams to design for personalization from day one.

The Section Your PRD Is Missing

A traditional software PRD has functional requirements (what the system does), non-functional requirements (how fast, how reliable), and user stories (who benefits and why). AI product PRDs add model requirements (accuracy, latency, hallucination rates) and data requirements (training data, evaluation sets).

The missing section is user understanding requirements, what the AI needs to know about each user to deliver its functional requirements well.

Think about it this way. If you are building an AI that recommends financial strategies, your PRD probably says the AI should provide personalized investment recommendations. But personalized based on what? Risk tolerance? Net worth? Life stage? Financial literacy level? Investment goals? Prior experience with similar products?

If you do not specify what personalized means: what dimensions of understanding the AI needs, when it should learn them, and how the depth of understanding should grow, then personalized means whatever the engineer who implements it decides. And it usually means very little.

How to Write User Understanding Requirements

User understanding requirements answer five questions:

What should the AI know? Define the dimensions of understanding. For a productivity AI, this might be work style, communication preferences, project context, expertise areas, and recurring patterns. For a health AI, it might be health goals, conditions, lifestyle constraints, knowledge level, and emotional state.

These dimensions should be specific to your product. Do not write the AI should understand the user. Write the AI should maintain a model of the user’s expertise level (beginner/intermediate/expert) in each of 4 product domains, updated after every interaction.

When should it learn? Specify the learning curve. What should the AI know after one interaction? After five? After thirty days? If you do not set these milestones, the team has no target to build toward.

The best AI products have a deliberate learning progression. After the first interaction, they know the user’s primary goal. After five interactions, they know their communication preferences. After thirty days, they can anticipate needs.

How should it learn? Define the learning mechanisms. Should the AI ask explicitly? Infer from behavior? Combine both? What signals should it use? How should it handle uncertainty?

Explicit learning means asking the user. Implicit learning means observing. Most products need both, weighted differently at different stages. Early interactions favor explicit learning because you have no behavioral data. Later interactions shift toward implicit learning as the model has context.

How should knowledge improve the experience? For every understanding dimension, specify how it changes the AI’s behavior. If the AI knows the user is an expert, what specifically changes? Shorter explanations? More technical vocabulary? Faster escalation to complex topics?

This is where most hand-waving happens. Teams say the AI should personalize without specifying what personalization looks like in practice. Be concrete: when expertise level is expert, skip introductory explanations and lead with the insight.

How do you measure understanding quality? Define what good understanding looks like. The fit dimension of the alignment score measures this, but you need product-specific criteria. A user model that is 80% accurate on expertise level but 40% accurate on communication preferences has a specific shape that suggests specific improvements.

1// Define what your AI should know about each user← The missing section in your PRD
2const requirements = {
3  byInteraction3: {
4    dimensions: ['primary_goal', 'expertise_level', 'role'],
5    method: 'explicit',  // ask during onboarding
6    minConfidence: 0.70
7  },
8  byInteraction10: {
9    dimensions: ['communication_style', 'depth_preference', 'constraints'],
10    method: 'hybrid',  // infer + confirm
11    minConfidence: 0.75
12  },
13  byDay30: {
14    dimensions: ['patterns', 'predictions', 'preferences'],
15    method: 'implicit',  // observe behavior
16    minConfidence: 0.80
17  }
18};

The Downstream Impact

When you add user understanding requirements to your PRD, everything downstream changes.

Architecture changes. The engineering team now knows they need a self-model layer, not as a nice-to-have, but as a core system component. They design for it from day one instead of trying to bolt it on after launch.

Evaluation changes. QA can now test personalization quality. Instead of checking whether the AI generates a correct response, they can check whether the AI generates the right response for this specific user. The fit dimension of the rubric becomes testable.

Prioritization changes. Product decisions now weigh user understanding impact. A feature that improves understanding (like better onboarding questions) gets prioritized alongside features that improve model accuracy.

Roadmap changes. The product roadmap becomes an understanding roadmap. Version 1 knows your role and goal. Version 2 knows your communication preferences. Version 3 anticipates your needs. This is a clearer story than Version 1 has chat. Version 2 has better chat. Version 3 has the best chat.

A Template for the Missing Section

Here is a starting template you can add to any AI PRD:

User Understanding Requirements

Dimensions: List the specific aspects of each user the AI should model (goal, expertise, style, constraints, context, history).

Learning Curve:

• By interaction 1: know [dimensions] via [method]
• By interaction 5: know [dimensions] via [method]
• By day 30: know [dimensions] via [method]

Personalization Impact: For each dimension, describe how the AI behavior changes when the understanding is high versus low.

Quality Targets:

• Fit score above 0.60 by interaction 5
• Fit score above 0.70 by interaction 20
• Fit score above 0.80 by day 90

Measurement: How will you validate that the AI actually understands each dimension? What user feedback or behavioral signals confirm understanding quality?

Trade-offs

This section adds complexity to the PRD process. Writing user understanding requirements takes time and forces difficult conversations about what personalized actually means. But the alternative, shipping a generic AI and retrofitting personalization, is more expensive by an order of magnitude.

Not all understanding is equally valuable. Some user dimensions matter enormously for your product (expertise level for an educational AI), while others barely matter (timezone for a calculation tool). Prioritize ruthlessly. Start with 3-5 dimensions that most impact quality.

Understanding requirements evolve. What you need to know about users changes as the product matures. The requirements should be treated as living documents, updated as you learn what dimensions actually predict quality improvements.

There is a privacy tension. The more you know about users, the more responsibility you carry. User understanding requirements should be paired with explicit data governance policies, what you collect, how you store it, and when you delete it.

What to Do Next

1. Audit your current PRD. Open your latest AI product spec and search for the word understand or personalize. If those words appear without specific dimensions, timelines, and quality targets, you have the gap. Add the missing section using the template above.

2. Define your top 5 understanding dimensions. What are the 5 things your AI would need to know about each user to deliver a genuinely personalized experience? Write them down, then for each one, describe what changes in the AI’s behavior when it knows versus does not know.

3. Instrument the fit dimension. Start measuring how well your AI matches its output to each specific user. The fit score is the metric that holds your team accountable for the understanding requirements you defined. See how Clarity powers user understanding infrastructure.

Every AI PRD defines what the model should do. The best ones also define what the model should understand. Add the missing section.

References

1. Product vs. Feature Teams
2. only 1 in 26 unhappy customers actually complains
3. Qualtrics notes in their churn prediction framework
4. Continuous Discovery Habits
5. 80% of features in the average software product are rarely or never used

Related

• The AI Product Discovery Process That Actually Works
• Why AI Products Churn Faster Than SaaS
• The Feedback Loop That Compounds