From MVP to VLP: Building Viable Learning Products in the AI Era

If you’re building an AI product and starting with an MVP - stop and think again.

The Minimum Viable Product (MVP) framework has served startups well for over a decade. Build the smallest version of your idea, ship it, and learn from how users respond. It’s a fast, lightweight way to validate early ideas.

But here’s the problem: AI products aren’t like traditional apps. The user experience is powered by probabilistic systems that evolve over time. The “value” of your product isn’t just what it does today - it’s how well it learns from use.

That’s why more AI-native teams are adopting a new approach: the Viable Learning Product, or VLP.

What Is a Viable Learning Product?

A Viable Learning Product is the first version of an AI product designed to learn.

It’s not just about launching something minimal - it’s about launching something that gathers meaningful signals, teaches your model, and reveals where your data workflows succeed (or fail).

Where an MVP asks, “Do users want this?”, a VLP asks:

• “Is the model improving over time?”

• “Are we collecting useful data?”

• “Are we shaping the right behaviors?”

A VLP is a living system. It’s designed not just to prove viability - but to accelerate learning.

MVP vs. VLP: What’s the Difference?

Here’s how the two approaches differ:

• Goal

  • MVP: Focuses on validating product-market fit.

  • VLP: Focuses on validating the learning loop and the quality of training data.

• Feedback Loop

  • MVP: Collects feedback from users to inform the business.

  • VLP: Collects feedback from users to train the model - and inform both the system and the business.

• Key Metrics

  • MVP: Measures success through engagement, usage, and retention.

  • VLP: Measures success through signal quality, correction rates, model confidence, and learning velocity.

• Primary Risk

  • MVP: Risk of wasting time building the wrong features.

  • VLP: Risk of collecting poor or irrelevant data, leading to a stagnant or inaccurate model.

• Outcome

  • MVP: Helps you launch quickly.

  • VLP: Helps you learn quickly - and improve the product through iteration.

What Makes a VLP Work?

Here are the essential components:

1. Minimal but Intentional UX

VLPs are still lean - but the UI is designed to guide users into generating useful data. That might mean:

• Structured input fields (instead of free text)

• Confirm/correct flows to validate model output

• Clear moments where users provide feedback, ratings, or edits

2. Signal-First Design

Every user interaction should answer: Is this helping us train a better model?

You don’t need perfect behavior yet - but you do need:

• Tracking of inputs and outputs

• Ways to capture intent (what users meant) vs. outcome (what model did)

• Flags or metrics around low-confidence or error-prone moments

3. Observability and Feedback

A VLP needs to observe:

• When things go wrong

• Where the model performs well or poorly

• How users adjust or work around issues

It’s not enough to log usage - you need to understand learning.

Building a VLP: Step by Step

Here’s how to build with a VLP mindset.

Step 1: Define What the Model Needs to Learn

What’s the core model behavior you care about? Ranking, tagging, summarizing, predicting?

Map this to desired user behaviors and define what good signal looks like.

Step 2: Design the Signal-Capturing Flows

Create flows where user actions give you direct or indirect supervision. Examples:

• Approve/reject results

• Highlight correct answers

• Edit outputs inline

• Rank or compare alternatives

Step 3: Build the Simplest UI That Captures Useful Data

Don’t overdesign. But do:

• Make feedback actions intuitive

• Nudge users to interact in ways that create learning value

• Avoid “dead-end” outputs - always allow course correction

Step 4: Measure Model Learning, Not Just UX

Look at:

• Change in model confidence or accuracy

• % of outputs corrected by users

• Coverage of key edge cases

• Correlation between usage and quality

Step 5: Iterate on Learning, Not Just Features

Your roadmap shouldn’t just add features - it should improve learning fidelity:

• Cleaner feedback loops

• Faster training updates

• Better signal labeling

• UX flows that teach more, faster

Real-World Examples of VLPs

A Tagging Assistant

The first version only highlights categories. Users approve or change them. Every edit improves the model.

A Recruiting Tool

It ranks candidates based on resumes and preferences. Users reorder them - the system learns what "good fit" means.

A Product Insights App

Users submit feedback. The tool tries to tag sentiment and theme. Users correct mistakes - and the model adapts weekly. These are simple. But they build the feedback loops first, which is what ultimately powers product success.

Conclusion: Build to Learn, Not Just to Launch

In AI, your product isn’t static - it’s trained. That means your “first version” should be more than viable - it should be valuable to the system’s learning process.

A Viable Learning Product isn’t just a fancy MVP. It’s a smarter way to start.

So if you’re building an AI product, don’t just ask “What’s the smallest version we can ship?”

Ask:

“What’s the smallest version we can ship that learns something meaningful?”

That’s how you set your model - and your team - up for long-term success.