AI Product Idea Document

Objective

By the end of this portfolio project, you will have a clear AI product idea document that explains a user problem, an AI approach, expected value, risks, and a first evaluation plan.

Prerequisites

Before this project, you should complete all ten lessons, the AI Foundations quiz, and the mini project.

Plain-English Explanation

This project turns the module into a portfolio artifact. You are not building the product yet. You are proving that you can think like an AI product builder: start with the user problem, choose the right technique, explain the workflow, and name the risks.

Key Terms

Portfolio Artifact

A portfolio artifact is work you can show later to demonstrate how you think and what you can build.

Evaluation Plan

An evaluation plan explains how you will test whether the AI feature is accurate, useful, and safe enough for its purpose.

Mental Model

Think of this as a one-page product brief for a team that might build the feature. The brief should be specific enough that an engineer, designer, or founder can understand the problem and the first version.

Example

Product idea: FurOrbit appointment triage assistant.

The assistant reads appointment notes, identifies urgent language, suggests a category, and drafts a summary for clinic staff. It uses classification and an LLM draft, but a human still reviews urgent or medical output before action.

Code Walkthrough

No implementation is required yet. You can describe the system as a flow:

User input -> AI technique -> Product action -> Human review -> Success measurement

For example:

Pet owner note -> Classification + LLM summary -> Staff triage queue -> Vet tech review -> Faster routing with fewer missed urgent cases

Common Mistakes

• Starting with the AI technique instead of the user problem.
• Choosing an agent when a simpler model or rule would work.
• Forgetting evaluation and safety.
• Writing a vague idea that cannot be tested.

Exercise

Write a product idea document with these sections:

1. Product name.
2. Target user.
3. User problem.
4. Current workflow.
5. Proposed AI behavior.
6. AI technique: rules, ML, embeddings, RAG, LLM, or agent.
7. Why this technique fits.
8. Required data or documents.
9. Main risks and guardrails.
10. Success metric.
11. First version scope.

Reflection

What is the smallest version of this AI feature that would still create real value?

Further Reading

• Google PAIR: People + AI Guidebook
• Microsoft HAX Toolkit
• NIST AI Risk Management Framework