How to Prototype AI Features Cheaply

Most AI feature ideas we see arrive with an architecture attached, including vector databases, model hosting and data pipelines, before anyone knows if users want the output. To prototype AI features means testing the value and the interaction of an AI capability with the cheapest possible stand-in for the real system, so that infrastructure decisions are made with evidence instead of hope.

The stand-in can be a person, a spreadsheet or a hosted model called from a script. What matters is that users see realistic outputs in a realistic workflow. This post covers why infrastructure-first AI projects go wrong, a staged way to prototype, how to measure a prototype, a worked example and the mistakes that waste the most money.

Why AI Projects Should Not Start With Infrastructure

Production AI infrastructure is expensive to build and expensive to change. Data pipelines, retrieval systems, model hosting and monitoring each add operational work, and all of it is wasted if users do not value the feature.

The usual advice from people who build these systems is to start simple. Anthropic's engineering guidance recommends finding the simplest solution possible, and only increasing complexity when needed. Google's machine learning rules make a similar point, noting that a simple heuristic can get your product out the door.

We would add one more reason. AI outputs are unpredictable, and users react to them in ways teams do not expect. That is learned fastest with a flexible prototype, not with a pipeline that took months to build.

How to Prototype AI Features in Six Steps

Prototype AI features by moving through a human stand-in, then a hosted model with prompts and an evaluation set, then an infrastructure decision, and move forward only when each step answers its question. We use the same six steps for summaries, extraction, recommendations and assistants.

1. Define the Interaction and the Output

Write down the user moment the feature changes, the input it receives and the exact output it should produce. Include the format, the length and what a good answer looks like.

A precise output definition becomes the first draft of your evaluation criteria. Without it, every later stage turns into a debate about taste.

2. Fake the Intelligence With a Person

Have a person produce the outputs behind a working interface, either live or prepared in advance for a fixed set of inputs. Nielsen Norman Group describes this Wizard of Oz approach as a way to test an interface that appears to be autonomous but is fully or partially controlled by a human.

This stage answers the value question. If users ignore perfect, human-quality outputs, a model will not save the feature.

3. Measure Value Against a Baseline

Compare the workflow with and without the feature, using a metric users care about, such as time to complete a task, errors caught or decisions made. Collect qualitative feedback on what the outputs missed.

Stop here if the value is weak. That is a successful prototype, because it saved the cost of building the wrong thing.

4. Swap in a Hosted Model and Simple Prompts

Replace the person with a call to a hosted model, using straightforward prompts and the data you already have. Avoid fine-tuning, custom retrieval or self-hosting at this point.

This stage answers the feasibility question, which is if an off-the-shelf model can approach the quality the human stand-in delivered.

5. Build a Small Evaluation Set Early

Collect a set of real inputs with known good outputs, including tricky and edge cases. Run every prompt or model change against it and track the results.

An evaluation set turns quality from an impression into a number, and it carries straight into production. We cover the testing side in how to test AI features when outputs are non-deterministic.

6. Decide on Infrastructure Last

Only once value and feasibility are proven do you choose the production architecture. Look at volume, latency, cost per call, data privacy and how much control you need over the model.

Many features never need more than a hosted model, good prompts and a clear fallback. Some do need retrieval, fine-tuning or dedicated hosting, and by now you will know which and why.

Write the decision down with the numbers that drove it, such as expected calls per day, acceptable response time and the quality score on the evaluation set. When those numbers change later, the team can revisit the architecture on evidence instead of starting the debate again.

Choose a Stand-In That Fits the Feature

The right stand-in for a prototype depends on what the feature does. We pick the cheapest option that still gives users realistic outputs in their real workflow.

In every case the stand-in produces something we can reuse. Hand-written outputs become the first entries in the evaluation set, and the questions users ask become the test cases the real model has to pass.

What a Good AI Prototype Measures

A prototype is only useful if it produces a decision. We set three measures before the first user sees it.

• A value measure, such as time saved or errors avoided, compared with the current workflow.
• A quality measure, scored against the evaluation set and reviewed by someone who knows the domain.
• A cost measure, estimating cost per use at expected volume, including human review of wrong answers.

We also record failure cases in detail. The ways an AI feature fails tell you what fallbacks and guardrails it needs, which is the subject of how to build AI features that degrade gracefully.

Keep the prototype honest about who it was tested with. Results from a handful of enthusiastic internal users can overstate value, so we try to include at least some users who were skeptical of the idea, and we note which feedback came from which group.

If any of the three measures is weak, the prototype has done its job by surfacing the problem before the expensive build.

A Worked Example From Support Ticket Summaries

Here is a hypothetical example, not a client story. A SaaS company wants AI summaries of long support tickets so agents can get up to speed faster. Instead of building a model integration, the team prototypes.

A product manager writes summaries by hand for a batch of real tickets, and the summaries are shown in a staging version of the agent interface during an internal pilot. Agents use them for a short period, and the team interviews them and watches their workflow.

Agents find the summaries useful but point out that some miss critical context, such as earlier promises made to the customer.

That feedback becomes the first rule in the evaluation set. Only then does the team try a hosted model against the same tickets, and it builds production infrastructure once quality approaches the hand-written baseline. We like this example because every step answered one question at a low cost.

Mistakes That Make AI Prototypes Expensive

The expensive mistakes come from skipping stages or measuring the wrong thing. These are the ones we see most often.

• Building pipelines, retrieval or hosting before testing if users value the output.
• Putting raw model output in front of users without a human stand-in or review step.
• Optimizing model metrics before confirming that the feature changes a user outcome.
• Launching without a value, quality and cost measure, so nobody can decide what to do next.
• Treating the model as a black box, so failures cannot be explained or fixed.

Sometimes the deeper question is if the feature should use AI at all, which we cover in when not to use AI in your product. A staged prototype is how we approach product engineering for AI features, because it keeps the expensive decisions until the cheap ones are made.

Prototype AI Features Before You Build the Platform

To prototype AI features without committing to infrastructure, define the output, fake it with a person, measure value, swap in a hosted model with an evaluation set and decide on architecture last. Each stage should either earn the next one or stop the project cheaply.

We think that sequence saves more money than any infrastructure choice. If you want help prototyping an AI feature for your product, Better Software can work through it with you.

Frequently Asked Questions

What Is Wizard of Oz Prototyping?

Wizard of Oz prototyping is a method where users interact with a system that seems automated, while a person produces the responses behind the scenes. We use it to test if an AI feature is valuable before building any model or infrastructure behind it.

What Are AI Prototypes?

AI prototypes are early, low-cost versions of an AI feature built to test value and feasibility. We build them with a human stand-in or a hosted model and a small evaluation set, so a team learns what users need before investing in production systems.

How to Build an AI Prototype?

We start by defining the exact input and output, then fake the output with a person behind a real interface. If users value it, we swap in a hosted model with simple prompts, score it against an evaluation set and only then plan the infrastructure.

What Are AI Prototyping Tools?

AI prototyping tools include hosted model APIs, prompt playgrounds, no-code app builders and design tools that generate interfaces. We use them to move fast, but we still define the output and an evaluation set first, because a tool cannot tell you if users value the result.

What Is Prototype Development?

Prototype development is building an early version of a product or feature to test an idea before full investment. We treat each prototype as an answer to one question, such as value, feasibility or cost, and we keep it as cheap as that question allows.