Most teams asking about AI do not have a blank page. They have a product in production, real customers, and a list of places where a model might help. The question is not "how do we build an AI product" — it is "how do we add AI to this one without destabilising it."
The good news is that you rarely need a rewrite. The work is mostly integration and judgement about where a probabilistic tool belongs.
Start where a mistake is cheap
Language models are useful and occasionally wrong. So the first place to apply them is where a wrong answer costs little and a human is still in the loop: drafting a reply someone will edit, summarising a long record, suggesting a category a user can override, surfacing search results.
Save the high-stakes, must-be-correct paths — billing, permissions, anything irreversible — for later, if at all. Those are decisions, not suggestions, and they should stay deterministic.
Ground the model in your own data
A general model does not know your product, your customers, or last Tuesday's ticket. To be useful it needs your context at the moment of the request — the relevant document, record, or history retrieved and passed in. This retrieval step is usually where the real engineering is, and where a feature goes from a demo to something dependable.
- Retrieve the specific context a request needs, rather than hoping the model already knows it.
- Keep the source of truth in your own system. The model reads from it; it is not the record.
- Show users where an answer came from, so they can trust it or check it.
Treat it like an external dependency
A model provider is a third-party service, and it will occasionally be slow, rate-limited, or down. The same discipline you apply to any integration applies here: timeouts, a sensible fallback when the call fails, and a product that degrades to its previous behaviour rather than breaking.
Design the AI feature so that when the model is unavailable, the product is merely less helpful — not broken.
Measure whether it actually helps
The honest test of an AI feature is not that it demos well. It is whether people keep the output, edit it lightly, or throw it away. Instrument that from day one. It tells you which features to keep, which prompts to fix, and where the model is quietly making things worse.
Cost is a design input, not an afterthought
Per-request model cost is small until it is multiplied by real traffic. It is worth estimating early: which calls are expensive, which can be cached, and which can use a smaller, cheaper model without anyone noticing. These are design decisions, and they are much easier to make before launch than after the first bill.
The short version
Add AI where a wrong answer is cheap, ground it in your own data, wrap it like any other dependency, and measure whether people keep what it produces. Do that and you get real value from AI without betting the product you already have on it.