By Muhammad Faraz · 3 min read
Choose one job for the model
Google’s Gemini API supports generative AI features such as text generation and multimodal input. That creates many possibilities, but a useful prototype begins with one task. For example, try turning a short product brief into a structured list of questions. Define what a good answer contains before choosing the interface. See the official Gemini documentation linked below for supported capabilities.
Source: Gemini API documentation ↗Create a small evaluation set
Prepare several realistic examples with different levels of detail. Include incomplete input, unfamiliar terminology and requests outside the intended scope. Write down what you expect from each case. Compare generated results against that checklist instead of selecting only the most impressive demonstration. A prototype should reveal where the idea fails as well as where it works.
Design the review experience
Give users an opportunity to edit or reject the result. Show the original input alongside the generated output when comparison matters. If the system cannot complete the task, offer a clear next step. A generic error or a confident but unsupported answer leaves the user doing the product’s recovery work.
Plan beyond the demo
Before launch, assign responsibility for credentials, usage limits, feedback and ongoing evaluation. Decide which data the feature actually needs and avoid sending unrelated customer information. Check current provider documentation when implementing. The prototype proves a possible interaction; it does not establish production reliability or a fixed operating cost.
A worked example: turn a brief into questions
Imagine a founder uploads a short description of a delivery app. Your feature should identify the people involved, separate known requirements from assumptions and propose the five questions that would most affect the estimate. Provide an example brief where the delivery region is missing and another where the region is explicit. A successful result asks about the missing region only in the first case. This is more useful evidence than a fluent paragraph describing a delivery app in general.
Make the output usable by another person
Give each proposed question a reason and connect it to a decision. “Will restaurants manage their own menus?” affects operational roles and screens; “What colors do you like?” belongs to a later design conversation. Keep the result short enough to review and let the founder edit it before sharing. Store the approved version separately from the generated draft so collaborators can tell which decisions are confirmed. Do not treat the model’s suggested requirements as an agreed scope.
A simple scorecard for the pilot
Review whether the output stays within the brief, notices missing information, avoids unsupported commitments and follows the requested structure. Use the same scorecard on every example. Ask a second reviewer to assess a few outputs independently; disagreement often exposes a vague requirement in your evaluation. Record failures in ordinary language. “Invented a payment provider” gives the team something to fix, while “the AI was bad” does not identify the underlying behavior.
What to hand over at the end
Deliver the prototype together with its example inputs, review criteria, known limitations and the decision about whether to continue. Name the person responsible for approving changes to the prompt or connected content. Keep model choice separate from the business goal so the feature can be evaluated again when implementation options change. The strongest next step may be a narrower task, a better input form or a simpler rule-based feature, rather than adding more generative behavior.
