Building an AI-first product feels like working with a new kind of clay. You don't really know how things will turn out until it's fired, if it ever makes it that far.
We all know these models are non-deterministic, and that every prompt is highly sensitive to its inputs—agents even more so. Instead of forcing the system to follow an arbitrary spec, you end up shaping its general behavior, learning how it responds as you go. Then add the open-ended range of inputs from real users, and things become even less predictable. Those nuances only reveal themselves through interaction. Trying to explain them on paper will always fall short. It's simply easier, and far more useful, to show people what the system does.
Prototypes, even at low fidelity, do that. They let teams, users, and stakeholders see the behavior instead of imagining it, and they create a shared reference point of what the product might become. Everyone learns faster, communicates better, and asks smarter questions.
The good news is that prototyping is a lot easier today. With AI coding assistants currently available, you can spin up a working demo in hours or days instead of weeks. It lowers the barrier to trying things out and speeds up how teams learn.
Prototyping isn't unique to AI products; the benefits are obvious in any kind of software. But for AI-first products, it's different. Prototyping is how discovery happens. It's how you figure out whether an idea will work, what to build instead, and sometimes realize your product can be smarter than you expected.
Learning through interaction
Working with AI is less about defining a perfect spec and more about developing intuition. Watching how the system reacts to different inputs, proprietary data, and a variety of instructions helps you build a sense of what to expect. Over time, you start to see patterns — not an infallible formula, but a range of possible outputs and tendencies. That variability can be uncomfortable for teams used to certainty, but it's also what makes the work fascinating.
Prototyping exposes these edges early. You quickly learn the system's common failure and success modes — where it's oversensitive to user inputs, or where issues in data curation, retrieval, or context management show up. These findings let you demonstrate how the system behaves as you tweak it, highlight use cases that need deeper evaluation, and give you something tangible to benchmark against existing products and solutions.
Sometimes you'll find approaches that make the product unfeasible. Other times, you'll uncover opportunities you didn't anticipate — a capability that could become the product's core. Either way, you end up with a clearer sense of what to build than you would by reasoning it out on paper.
Improving communication and alignment
Prototypes help create a common language between designers, engineers, and business stakeholders. Ideas stop being discussed in the abstract when you can point to specific behavior: "See what happens when you ask it this way?" or "This is how it handles ambiguous inputs."
For people who aren't deep in the inner workings of AI, that is deeply transformative. They don't have to imagine what context poisoning looks like, or how reasoning gets baked in — they can watch it happen. It helps align expectations, surface trade-offs, and make conversations more concrete. Every tweak or question can be answered by adjusting the prototype and showing what changes.
That's the real advantage: clarity through demonstration. Once the larger team sees how an AI system behaves, they stop wondering if it will work and start figuring out how to make it better.
The level of fidelity for AI prototypes
During discovery, prototypes don't need to be elegant. You can test a lot through the chat interfaces from major labs, their consoles, or with no-code tools. You don't want to delay learning for the sake of polish — not at this stage.
As workflows become more complex, you'll likely move into code-based prototypes. I often build these using coding agents (Cursor and Claude Code are my go-tos, but whatever tool helps you move fastest is the right one). Early versions are usually simple: a command-line tool, a small script, or an API endpoint. There's no need to over-index on the UI until you're closer to the kind of outputs worth pursuing. And it's important not to get attached — you might need to drop the prototype entirely and start fresh.
Once the idea starts to mature, I move to simple UI prototypes that make it easier to run user testing or let non-technical users try things on their own.
This kind of exploration is productive. It surfaces assumptions, exposes limitations, and builds intuition for how AI can fit into real workflows. The insights you gain will inform design, data strategy, and product direction. By the time you move into structured development, you already know what's worth building.
Prototyping isn't about speed for its own sake — it's about depth. You're reducing wasted effort by learning earlier, not just moving faster.
Keeping it alive
With AI, discovery doesn't end at launch. Models shift, frameworks evolve, and new capabilities appear all the time. That's why prototyping should stay part of the product's life — a recurring habit, not a phase.
Each time a new model or feature emerges, use prototypes to test what's possible and sense where the product might evolve. That cycle of discovery and rediscovery keeps you close to the technology and ahead of the curve. The best teams make it a shared practice — lightweight, ongoing, and collaborative.
A closing thought
Prototyping is how you think through an AI product. It turns what's abstract or hard to explain into something you can see and test. The model stops being a black box and becomes a material you can shape — alive, inconsistent, but revealing when you work with it.
In the end, you make sense of it by making something.
The first prototype rarely becomes the product, but it always helps you understand what you're really building.