What Makes AI Products Work: Your Domain Knowledge, Not the Model
By Amin Rabinia · Founder, Glissando AI
When an AI product gives inconsistent results, the instinct is almost always the same: try a better model. Upgrade the version, add examples to the prompt, tune the settings. Sometimes that helps. Usually it doesn't, because the model was never the bottleneck.
The models are available to everyone. Your competitors can use the same ones tomorrow. What they can't use is what you know: how your customers describe what they want, how your experts make decisions, what "good" means in your field. That knowledge is what makes an AI product work, and it is the part of the product you own.
Where the Model Runs Out
I learned this building the IQ Design platform. It takes an inspiration photo, a living room someone saw on Instagram or a page from a design magazine, and matches it to real products in a catalog.
It sounds like a computer vision problem, and partly it is. But the harder problem was language. Customers describe furniture in emotional, subjective words: "cozy," "warm," "Scandinavian," "the vibe of a cabin." A product catalog is organized by material, category, dimensions and price.
No model, however capable, can bridge that gap on its own. "Cozy" doesn't map to a SKU. Something has to define what "cozy" means in terms the system can use, the same way, every time. That something is a taxonomy, and building it was slow, unglamorous work.
What a Taxonomy Is, in Plain Terms
A taxonomy is an explicit, structured vocabulary for a domain that previously only existed in people's heads.
For IQ Design, that meant four things:
- Artifacts: the things being described, such as silhouette, material, finish, proportion and color palette.
- Definitions: what each one means, precisely.
- Guidelines: how they relate to each other.
- Syntax: how they are expressed so a model can use them consistently.
Before it existed, "cozy" meant something different to every designer who used the word. After it existed, it had one structured meaning the system applied across thousands of photos and thousands of products. That is the move from tacit knowledge, real and valuable but inconsistent, to explicit structure a system can rely on.
Your Expertise Is the Asset
This is the part most founders underestimate. If your business runs on expertise, you already have the most valuable input to an AI product. It just isn't in a form a machine can use yet. It might be:
- a method you teach or a framework you've written about,
- a book or a course,
- a process your team follows that nobody has written down,
- years of judgment about what a good result looks like,
- your own data, labeled the way only you would label it.
Structured properly, that knowledge becomes an asset the business owns. And an asset can power more than one product: an assistant, an evaluator, a recommendation engine, a training tool. The app is one output of it, not the thing itself. A competitor can copy an interface in a week. They can't copy a domain model built from years of your expertise.
The Decision That Changed the Results
The turning point on IQ Design wasn't a model upgrade. It was the decision to step back and map the domain before writing a single prompt: what the artifacts are, what they mean, how to use them, how to express them. That research took real time. It wasn't the part that looked impressive in a demo. But it saved months of patchwork that would have followed without it.
I've seen the other path too. Another team tackled a similar problem with prompt engineering alone, asking a model to interpret aesthetic descriptions with no structured definitions underneath. The same photo could produce different matches depending on small wording changes. There was no shared vocabulary to reason from, so results were inconsistent and hard to debug.
This matters most where taste, quality or judgment is involved, because that's where subjective description quietly undermines accuracy. No amount of prompt tuning fixes a definition problem. Prompting isn't product design, and it isn't domain modeling either.
Why This Beats Chasing a Better Model
Model choice feels like the lever that matters because it's visible: a model name, a benchmark score, a version number. Domain structure is invisible. Nobody sees the taxonomy; they just see whether the product works.
But a better model fed the same ambiguous inputs produces the same ambiguous outputs, with more confidence. Foundation models are extraordinarily capable, but capability isn't domain knowledge. They don't know what "cozy" means to your customers, in your catalog, in your market. You have to tell them, in a structure they can use.
This is also why the choice between RAG and fine-tuning often matters less than people expect. Both are ways of giving a model domain knowledge. Neither works well if the knowledge hasn't been structured first. The taxonomy comes before the architecture decision, not after.
What This Means for Your Product
If you're planning an AI product, put the domain work at the start, not at the end. In my builds it happens in the design step, before the first line of code, and the early phases of the build are where it gets tested on real data. The order is laid out in how to build an AI product without a technical team.
If an AI product you already have gives inconsistent results, ask one question before switching models: has anyone defined, explicitly and structurally, what the system is supposed to understand in this domain? If not, that's the work to do first.
It's slower. It's not the exciting part of a pitch deck. But it's the difference between an AI product that's consistently useful and one that's a coin flip dressed up as intelligence. If you'd like to see your own expertise working as the core of a product, that's what my free working demo is for.
This post is part of the AI Agents Guide, from the basics to the technical depth behind agents that actually work.