What should an agency deliver with Figma Make or v0 prototypes?

An agency using Figma Make or v0 should deliver a testable user flow, editable source, documented limits, and the findings that guide the next product decision. If training is included, the internal team should also be able to modify and review the prototype independently.

A convincing demo can answer whether an interaction feels promising. It can also hide a button that does nothing, a screen that always returns the same result, or an integration that exists only in the presenter’s explanation. The scope should say which behavior the team needs to experience and which behavior remains simulated.

What should a Figma Make or v0 prototype engagement include?

Choose a product question before choosing the tool. A team exploring an onboarding flow might need to compare a guided setup with a shorter form. A team investigating an AI assistant might need to test how people inspect and correct its proposed changes. Both assignments need clear tasks and believable states.

Figma describes Make as a prompt-to-code tool for functional prototypes, web apps, and interactive interfaces, using ideas or existing Figma designs. Its documentation also describes adding design-system packages. The agency should demonstrate how it brings the client’s design context into the prototype and where that context needs manual refinement.

v0’s Figma integration documentation describes extracting visual and structural context from a linked Figma file, including design tokens. It recommends dividing complex designs into components and refining them before composing a full interface. That gives a buyer a practical question: how will the agency keep generated parts consistent as the prototype grows?

For either tool, supply an approved brief, representative content, and the relevant components. If those components do not exist, make their creation part of the scope. The agency should explain whether it is testing an interaction, exploring a visual direction, or preparing code for engineering review. Those deliverables require different acceptance criteria.

• Provide a working link with the complete task the team wants to test.
• Deliver editable source and the design references used to create it.
• Label simulated data, unfinished integrations, and unsupported paths.
• Include realistic loading, error, empty, and recovery states where the task needs them.
• Record test findings and the changes made in response.

In an illustrative support-queue prototype, a reviewer should be able to open a request, change its owner, encounter a failed update, and recover without losing the request. The exercise can use synthetic records. Its purpose is to reveal the interaction, so everyone needs to know whether the update actually persists.

Ask the agency to revise one requirement during the evaluation. A changed permission or an extra approval step reveals how well the prototype can accommodate the product’s rules. Inspect the resulting flow and the source changes together. A new screen that breaks an earlier path creates another round of work.

Before any production use, engineering should review the code and integrations against the product’s own requirements. The engagement should identify the review owner and the remaining implementation work. Publishing a prototype link by itself does not establish that the application meets those requirements.

What should the team learn before the agency leaves?

Training should use the delivered prototype. Designers need to understand which context belongs in a prompt, how to make a bounded change, and how to check that the change preserved the intended behavior. They also need to recognize when the tool has introduced an assumption that the product team never approved.

A practical handoff includes the starting brief, an accepted example, and a flawed example with a written explanation. Keep the tool setup and source references with the project. The instructions should state where teammates can work, who reviews changes, and how to restore a previous working version.

Humbleteam’s AI infrastructure service includes workflow implementation and training for the client’s team. That supports evaluating it for adoption work. A team specifically buying Figma Make or v0 support should still ask for a demonstration in the chosen tool and include those deliverables in the proposal.

Independent practice is the acceptance test for that training. A participant should complete a different change while the trainer observes, then explain how they checked it. If the trainer must repeatedly take over, the team needs more practice or clearer instructions before the engagement ends.

Measure the full exercise, including preparation and repairs, and retain the review findings. The design-team AI training pilot guide covers that assessment. The final handoff should leave the internal owner with access to the project, the ability to repeat the workflow, and a written list of work that still needs engineering review.