From an LLM prototype to a working product: how to avoid mistakes

AI prototype can be assembled in an evening. But between a working demo and a product that people actually use and pay for, there's usually a huge gap — with a weak hypothesis, dirty data, unnecessary stack, and unclear metrics.

From Idea to Use Case The first and main mistake is starting with the model instead of the problem.

Developers often think: "Now I'll train an LLM or take a ready-made API, attach it to our product, and magic will happen." But that doesn't work. You need to first understand what exact pain your AI product solves.

Is this pain actually acute? Are clients willing to pay for the solution? How will they use it in real life, not in a sandbox?

The second stage is selecting a specific use case for your MVP. Many startups want to solve everything at once: text classification, prediction, generation, recommendations. That's a mistake.

Focus on one use case, one success metric, one target audience. This isn't a limitation—it's a strategy. This way you'll release your MVP faster, get feedback from real users, and be able to improve your product based on data, not assumptions.

Dirty

Data and Incorrect Metrics When you don't monitor datasets and metrics, everything falls apart. A model won't work better than the data it was trained on. If training data contains bias, labeling errors, or becomes outdated, the model will learn these problems and reproduce them in production.

This isn't an LLM-specific problem—it's a fundamental ML rule. The second insidious issue: incorrect metrics. You might look at accuracy, precision, recall and think everything is fine.

But a real user might simply not use the feature because it's slow, confusing, or doesn't integrate with their workflow. You need business metrics: feature usage, retention, willingness to pay. Third—absence of a baseline.

Before training a model, measure your baseline metric without AI. Maybe a well-tuned rule or simple classifier achieves 85% out of the 90% your use case requires? Don't waste a month on a neural network.

Or conversely, the baseline will show that you need a more complex approach.

• Dirty data—the model learns from errors and reproduces them in production Incorrect metrics—you're looking at accuracy, but the user cares about speed and convenience No baseline—you start from scratch instead of improving what exists * You forget about implementation—the algorithm is great, but it's impossible to integrate into the system ## Typical Project Killers Developers often test products under ideal conditions: clean data, small load, no edge cases. Then they deploy to production—and it turns out the model doesn't work on real data. Or the feature is completely unavailable to half the users because the designer forgot about it. Or metrics look good in logs, but nobody actually uses the product. Another mistake is over-complicating the stack. You don't need new tools for each stage: one framework for training, another for inference, a third for deployment, a fourth for monitoring. Choose tools that you and your team understand. Simplicity beats framework on top of framework.

What This Means AI products require a completely different approach than regular features.

Don't start with the algorithm—start with the problem. Honestly measure results on real data. And integrate implementation into the development process from the very beginning, not at the end when the model is trained but impossible to run in production. If you do this, you'll have not just a working prototype, but a working product.