Lovable + Amplitude: From Prototype to Evidence

AI-assisted building makes the first version faster. It does not tell you whether that version helps anybody. That is why I like putting Lovable and Amplitude in the same working loop: one shortens the distance to a real interface; the other helps the team observe what people actually do.

The useful unit is not a prototype followed by an analytics project. It is a build–instrument–observe–change loop designed before the first prompt.

1. Name the behaviour before the feature

Start with a sentence about the user, not the interface: “A participant can submit a project and understand what happens next.” Then name the few behaviours that would show progress.

• The user begins the flow.

• The user reaches the meaningful action.

• The user completes or abandons it.

• The system returns an error or needs recovery.

• The user comes back to continue.

This keeps the event plan small and connected to decisions. Tracking every click creates noise; tracking only page views hides the product.

2. Write the event plan in plain language

For each event, define its name, when it fires, the properties needed to interpret it and the question it should answer. Keep sensitive information out of event properties. Use stable identifiers and controlled values instead of copying free text into analytics.

A simple plan might include flow_started, project_submitted, submission_failed and result_viewed, with properties such as flow_version, entry_point and error_type.

Amplitude’s own guidance is blunt about the dependency: the events you choose determine which questions you can later answer.

3. Give Lovable the system, not only the screen

The build prompt should include the objective, important states, event plan, error behaviour, data boundaries and visual system. If the product already exists, provide the design tokens and the relevant flow instead of asking for a generic redesign.

Lovable can create the interface and integration quickly, but the team still owns the product decisions. Review generated database rules, authentication, server-side logic and secrets with the same care you would apply to hand-written code.

The current Lovable documentation describes the platform as producing editable code across frontend, backend, database, authentication and integrations. That makes ownership possible; it does not remove the need for review.

4. Validate tracking before reading a dashboard

Open the product as a test user and perform the flow. Confirm that the right events arrive once, with the expected properties and user identity. Test success, failure and retry paths. An attractive chart built on duplicate or missing events is worse than no chart because it creates confidence without evidence.

Amplitude’s Event Explorer is designed for this kind of real-time event inspection.

5. Put the evidence back into the build

Once real use begins, look for a decision, not a report. Where do people stop? Which entry point brings more meaningful completions? Does a new version reduce failure? Pick one finding and change the product. Then keep the event definition stable enough to compare.

What often goes wrong

• Analytics is added after the build, when nobody remembers the original decisions.

• Events are named after UI elements rather than user behaviour.

• Personal or free-text data leaks into event properties.

• The demo path works, but errors and retries are invisible.

• A dashboard is delivered without an owner or decision cadence.

• The team celebrates speed to prototype and never tests whether the problem was solved.

A live build is allowed to fail

In live sessions, a prompt sometimes returns the wrong result, an integration fails or an event does not appear. I do not hide that. The recovery is part of the work: inspect what happened, narrow the change, verify the data and try again.

That is more useful than a polished recording because teams need a method for imperfect systems, not faith that every prompt will work.

The smallest useful loop

• One owned user problem.

• One working flow.

• Three to five meaningful events.

• One validated dashboard or exploration.

• One product decision made from observed behaviour.

• One named owner for the next iteration.

I teach this workflow in the Maven session How to vibe code with Lovable and Amplitude. You can also read the shorter integration walkthrough.