In this article, I’ll walk you through what user flows are, why they matter, and three strategies for building them inside AI prototyping tools like Lovable and v0.
What is a User Flow?
A user flow is a sequence of steps that a user — or sometimes the system — takes to complete a task.
A simple flow might be linear: fill out a form, click submit, done.
A complex flow might include conditions and branches: if the user chooses this, show that; if they choose something else, take them down a different path.
A familiar example is onboarding. A user clicks Register, we ask for details, and depending on their answers we may request extra information, explain the product, or show follow-up steps.
Best Practices Before You Start
Before diving into strategies, a few ground rules:
Don’t overload your prompts.
If you describe a 20-step flow in one go, the AI will almost certainly fail. Instead, break it into smaller chunks — 2–4 steps at a time works best.Prompt, test, refine.
Build step by step, check outputs, and adjust as you go. This is faster than trying to fix a giant, messy prototype.Stay flexible.
Different situations call for different approaches. You might describe a flow in plain language, provide a diagram, or attach designs. Often, you’ll combine them.
Choosing Your Strategy
How do you know which approach to take? It depends on where you are in the process:
Already have mock-ups? Use them as the foundation and connect them with logic.
Starting with just a problem? Describe flows directly in the AI tool and shape the experience as you go.
Mapped things out? If you already have a mind map or flow diagram, use that to guide the logic.
There’s no single right answer. The key is to pick the strategy that matches your stage, and remember you can always mix and match.
Strategy 1: Describe in Plain Language
The simplest way to create a flow is to describe it in words.
For example, in a Hiking Events app, I might say:
The “Create Event” flow opens as a full-screen modal.
Include a back button, a progress bar, and a close button.
If the user tries to close while entering details, show a confirmation dialog with “Continue editing” and “Discard.”
Step one: ask the user to choose an event type — Hiking, Cycling, Climbing, Skiing, Bouldering, or Social Event.
From there, I can add logic. For example:
If the event type is Hiking, Cycling, or Climbing → show a route selection step.
If it’s Skiing, Bouldering, or Social → skip directly to choosing date and time.
Just by describing the flow step by step, the AI scaffolds a working experience.
Strategy 2: Use Diagrams
If you’ve already mapped the flow, you can use a diagram. A simple flowchart can show the sequence of steps and conditions.
⚠️ One important rule: keep logic and design separate.
If you try to include UI mock-ups inside your diagram, the AI will struggle to separate what’s design and what’s logic. Instead:
One diagram for logic.
Separate files for designs.
You can then tell the AI: “Read this diagram and implement the logic into the flow.”
Strategy 3: Work With Designs
Finally, you can build flows directly from mock-ups.
Here’s how:
Export your screens one by one.
Attach them to your prompt.
Explain the logic that connects them.
For example:
Step one: user selects an activity type (see design).
Step two: user enters event name, date, and number of participants.
The AI will use your design as a reference and build the flow accordingly.
If you need pixel-perfect fidelity, you can export components from Figma (using Figma-to-React or Builder.io) and feed the code into the AI. This gives you more control while still leveraging AI for the logic.
Mixing Strategies
In practice, you don’t have to choose just one strategy. You might:
Start by describing flows in plain language.
Add a diagram to clarify conditions.
Layer in designs to polish the visuals.
The key is to keep prompts focused, refine step by step, and combine methods as needed.
Final Thoughts
With these strategies, you can go beyond static screens and create prototypes that feel like real products — complete with branching logic, conditions, and user behaviour.
Plain language is great for quick scaffolding.
Diagrams help structure complex logic.
Designs bring polish and fidelity.
Mix them, break your flows into manageable steps, and refine as you go.
That’s how you build powerful, conditional flows in AI prototyping tools.
👉 Want to go deeper into AI prototyping? Join my upcoming 4-day cohort course, where I’ll share advanced techniques, real-world workflows, and practical strategies to help you build powerful, production-ready prototypes with AI. If you’ve enjoyed this tutorial, this course will take you several steps further — from mastering prompting tactics to integrating logic, design systems, and beyond.
🔗 4 days AI Prototyping course
Happy prompting!
