How detailed should the AI behavior specification be?

Detailed enough that an engineer can implement it without guessing. Specify input format, output format, quality requirements, and explicit scope boundaries. Vague specs like "the AI should be helpful" lead to inconsistent implementations and quality problems.

Should I use a third-party AI API or build my own model?

For most product teams adding AI features, start with a third-party API. It is faster to launch and iterate. Only consider custom models if you have unique data that provides a competitive advantage, you need to run inference at very high volumes where API costs become prohibitive, or you have strict data privacy requirements that preclude sending data to external providers.

How do I handle users who do not want AI features?

Always provide a way to disable AI features. Some users will prefer the non-AI experience, and that preference should be respected. Design your product so the AI feature is an enhancement, not a requirement.

What is the right quality bar for launching an AI feature?

The AI feature should be better than the alternative -- which is often doing nothing or doing the task manually. It does not need to be perfect. Set a specific accuracy threshold based on user research about what level of errors they will tolerate in exchange for speed or convenience. ---

AI Feature Spec Template

What This Template Does

Adding AI capabilities to an existing product is fundamentally different from building a traditional feature. You are introducing probabilistic behavior into a deterministic system. Users who trust your product because it behaves predictably must now interact with a component that may produce different outputs for the same input, may occasionally be wrong, and may behave in unexpected ways at the boundaries. A standard feature spec does not account for these dynamics.

This template provides a structured format for specifying AI features that integrate into existing products. It covers the unique requirements of AI feature development: how the AI component fits into existing user workflows, what quality bar it must meet, how it degrades gracefully, what data it needs access to, and how you will measure whether it actually helps users. Use it every time you add an AI-powered capability to a product that was not originally built around AI.

Direct Answer

An AI Feature Spec is a requirements document for adding an AI-powered capability to an existing product. It extends a standard feature spec with sections for model behavior, integration architecture, safety boundaries, data access requirements, and quality evaluation. This template helps you specify AI features that enhance your product without breaking user trust.

Template Structure

1. Feature Overview

Purpose: Define the AI feature, its relationship to the existing product, and the user problem it solves.

Fields to complete:

## Feature Overview

**Feature Name**: [Name of the AI feature]
**Product**: [Name of the existing product this feature is being added to]
**Feature Owner**: [Name and role]
**Target Release**: [Date or release version]

### What This Feature Does
[2-3 sentences describing the AI feature in user-facing terms]

### User Problem
[What problem does this solve? Why are users asking for this?]

### How It Fits Into the Existing Product
[Where does this feature appear in the product? What existing workflows does it enhance?]

### Success Criteria
- [Primary metric: e.g., 30% reduction in time-to-complete for task X]
- [Secondary metric: e.g., 80% of users who try the feature use it again within 7 days]
- [Guardrail metric: e.g., Support ticket volume does not increase by more than 5%]

2. User Experience Design

Purpose: Define exactly how users discover, interact with, and control the AI feature within the existing product experience.

Fields to complete:

## User Experience

### Discovery and Activation
- **How users find the feature**: [Button / Menu item / Contextual suggestion / Always-on]
- **Opt-in vs. opt-out**: [Must users explicitly enable it, or is it on by default?]
- **First-time experience**: [What onboarding or explanation do new users see?]

### Interaction Flow
1. [Step 1: User does X]
2. [Step 2: AI processes and returns Y]
3. [Step 3: User reviews / edits / accepts]
4. [Step 4: Result is applied to the product]

### User Controls
- [ ] User can undo AI actions
- [ ] User can edit AI outputs before they take effect
- [ ] User can disable the feature entirely
- [ ] User can provide feedback on AI quality (thumbs up/down)
- [ ] User can adjust AI behavior (e.g., creativity level, verbosity)

### AI Disclosure
- [ ] Feature is clearly labeled as AI-powered
- [ ] AI-generated content is visually distinct from user-created content
- [ ] Users understand that AI outputs may be imperfect

3. AI Behavior Specification

Purpose: Define exactly what the AI component does, what quality bar it must meet, and where its boundaries are.

Fields to complete:

## AI Behavior

### Input and Output
- **Input**: [What data does the AI receive? User text, product data, context, etc.]
- **Output**: [What does the AI produce? Text, suggestions, classifications, etc.]
- **Output format**: [Structured JSON, free text, list of options, etc.]
- **Max output length**: [Token or character limit]

### Quality Requirements
| Dimension | Requirement | How to Measure |
|-----------|-------------|----------------|
| Accuracy | [e.g., > 90% correct on test set] | [Automated eval + human review] |
| Relevance | [e.g., Output addresses user intent > 95% of the time] | [Human evaluation] |
| Tone and style | [e.g., Matches product voice and brand guidelines] | [Style guide audit] |
| Latency | [e.g., Response in < 3 seconds for 95th percentile] | [Performance monitoring] |
| Consistency | [e.g., Similar inputs produce similar quality outputs] | [Regression test suite] |

### Scope Boundaries
**The AI feature MUST**:
- [Required behavior 1]
- [Required behavior 2]
- [Required behavior 3]

**The AI feature MUST NOT**:
- [Prohibited behavior 1: e.g., Access data from other users]
- [Prohibited behavior 2: e.g., Make changes without user confirmation]
- [Prohibited behavior 3: e.g., Generate content that violates content policy]

**Edge cases to handle**:
- [Edge case 1: What happens when input is empty or nonsensical?]
- [Edge case 2: What happens when input is in a language the model does not support?]
- [Edge case 3: What happens when the AI cannot produce a confident answer?]

4. Integration Architecture

Purpose: Define how the AI component connects to the existing product technically.

Fields to complete:

## Integration Architecture

### System Design
- **AI provider**: [Third-party API / Self-hosted model / Hybrid]
- **Model**: [Specific model name and version]
- **API integration point**: [Where in your backend does the AI call happen?]
- **Data flow**: [User action -> Backend -> AI service -> Response -> Frontend]

### Data Access
| Data Source | What the AI Sees | Why It Needs It | Sensitivity |
|------------|------------------|-----------------|-------------|
| [Source 1] | [Specific fields] | [Context for generation] | [PII / Internal / Public] |
| [Source 2] | [Specific fields] | [Personalization] | [PII / Internal / Public] |

### Data Privacy Requirements
- [ ] User data is not used for model training
- [ ] Data sent to AI provider is minimized to what is necessary
- [ ] PII is stripped or anonymized before sending to external AI
- [ ] Data processing complies with GDPR / CCPA / relevant regulations
- [ ] Data retention by AI provider is understood and documented

### Performance Requirements
- **Latency budget**: [Total end-to-end time allowed, broken down by component]
- **Concurrency**: [How many simultaneous AI requests must the system support?]
- **Rate limits**: [AI provider rate limits and how to handle them]
- **Cost per call**: [Expected cost and monthly budget ceiling]

5. Error Handling and Fallbacks

Purpose: Define what users experience when the AI component fails or produces poor results.

Fields to complete:

## Error Handling

### Failure Scenarios
| Scenario | User Experience | Technical Response |
|----------|----------------|-------------------|
| AI service is down | [Feature gracefully hidden / Error message] | [Circuit breaker, fallback] |
| AI response is slow (> Xs) | [Loading indicator / Timeout message] | [Timeout after Xs, retry once] |
| AI returns low-quality output | [Show with disclaimer / Do not show] | [Log for review] |
| AI returns content policy violation | [Block and explain] | [Log incident, alert team] |
| Rate limit exceeded | [Queue or disable temporarily] | [Backoff, queue management] |

### Fallback Experience
[Describe what the product experience looks like when the AI feature is completely unavailable. The product must remain fully functional without the AI component.]

### Error Messages
- **AI unavailable**: "[Specific user-facing message]"
- **AI slow**: "[Specific user-facing message]"
- **AI error**: "[Specific user-facing message]"

6. Testing and Evaluation Plan

Purpose: Define how you will validate the AI feature before and after launch.

Fields to complete:

## Testing Plan

### Pre-Launch Testing
- [ ] Unit tests for integration code (API calls, error handling, data transformation)
- [ ] AI quality evaluation on [N] test cases covering [categories]
- [ ] Edge case testing for [list specific edge cases]
- [ ] Load testing at [X] concurrent users
- [ ] Security review of data flows to AI provider
- [ ] Accessibility testing for AI-generated content
- [ ] Cross-browser / cross-platform testing

### Quality Evaluation
| Test Category | Number of Cases | Pass Criteria | Method |
|--------------|----------------|---------------|--------|
| Happy path | [N] | [> X% correct] | [Automated + human] |
| Edge cases | [N] | [No critical failures] | [Manual review] |
| Adversarial inputs | [N] | [All handled safely] | [Red team testing] |
| Bias and fairness | [N per demographic] | [< X% variance] | [Demographic analysis] |

### Post-Launch Monitoring
- [ ] Dashboard tracking AI quality metrics
- [ ] Alerting on error rate spikes
- [ ] User feedback collection mechanism
- [ ] Weekly quality review of sampled outputs

7. Rollout Plan

Purpose: Define the phased release strategy for the AI feature.

Fields to complete:

## Rollout Plan

### Phases
| Phase | Audience | Duration | Entry Criteria | Exit Criteria |
|-------|----------|----------|---------------|---------------|
| Internal testing | [Team] | [1 week] | [Feature complete] | [No blocking bugs] |
| Beta | [X% or specific users] | [2 weeks] | [Internal sign-off] | [Quality metrics met] |
| GA | [All users] | [Ongoing] | [Beta metrics green] | [N/A] |

### Feature Flags
- **Flag name**: [Name of the feature flag controlling this rollout]
- **Kill switch**: [How to instantly disable the AI feature in production]
- **Rollback plan**: [Steps to revert if quality drops below threshold]

How to Use This Template

Start with the Feature Overview to ensure the AI feature has a clear user problem and success criteria. If you cannot articulate why AI is the right solution, consider a simpler approach first.

Design the user experience before specifying AI behavior. Start from what users need, not from what the model can do. The best AI features feel like natural extensions of the product, not bolted-on technology demos.

Define scope boundaries explicitly. What the AI must not do is as important as what it must do. These boundaries protect users and your product reputation.

Work with engineering on the integration architecture. Data flows, privacy requirements, and performance budgets must be realistic and agreed upon before development begins.

Write error handling before the happy path. The product must work without the AI feature. If removing the AI breaks the product, you have a design problem.

Plan for a phased rollout. Start with internal users, expand to a small percentage, monitor quality, then go broad. Never launch an AI feature to all users simultaneously.

Tips for Best Results

Start with a manual version of the AI feature. Before building AI automation, have a human perform the same task. This reveals the true complexity, establishes a quality baseline, and generates training data.

Design the feature to be useful even when the AI is mediocre. The best AI features present suggestions that users can accept, edit, or reject. Do not design flows where AI output is applied automatically without user review.

Set up A/B testing from day one. Measure whether the AI feature actually improves outcomes compared to the existing experience. It is common for AI features to feel impressive in demos but not improve real metrics.

Monitor costs weekly during rollout. AI API costs can spike unexpectedly as usage grows. Set budget alerts and have a plan for when you hit spending limits.

Collect user feedback on every AI interaction. A simple thumbs up/down on AI outputs gives you a continuous quality signal that is more valuable than any pre-launch evaluation.

Plan your prompt engineering iteration cycle. The first version of your prompts will not be good enough. Budget time for iteration based on real user data, not just internal testing.

Key Takeaways

AI features must fit naturally into existing product workflows -- they should feel like enhancements, not bolted-on demos

Define what the AI must not do as explicitly as what it must do

The product must remain fully functional when the AI component is unavailable

Design for user control: users should be able to review, edit, and reject AI outputs

Monitor quality continuously after launch -- AI feature performance can degrade over time as usage patterns change

About This Template

Created by: Tim Adair

Last Updated: 2/9/2026

Version: 1.0.0

License: Free for personal and commercial use

AI Feature Spec Template

What This Template Does

Direct Answer

Template Structure

1. Feature Overview

2. User Experience Design

3. AI Behavior Specification

4. Integration Architecture

5. Error Handling and Fallbacks

6. Testing and Evaluation Plan

7. Rollout Plan

How to Use This Template

Tips for Best Results

Key Takeaways

About This Template

Frequently Asked Questions

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