What to Ask Before Hiring an AI/GenAI Development Partner

A GenAI feature can look completely finished in a demo and still fail once real users hit it with real inputs — the gap between "the model answered correctly for the ten prompts I tried" and "this holds up in production" is where most GenAI project risk actually lives, and it's largely invisible until you ask about it directly. The questions below are aimed specifically at that gap, not at generic dev-partner vetting you'd do for any software project.

Questions about the model layer

• —Build vs. buy: are they proposing to fine-tune or train something, or orchestrate existing foundation models well? For most product ideas, the honest answer should be the latter — ask why if it isn't
• —Which model, and why that one — cost, latency, and quality trade off differently across providers, and "we'll pick the best one" without naming candidates is a sign the evaluation hasn't happened yet
• —What happens when the chosen model's provider changes pricing, deprecates a version, or has an outage — is the integration portable, or hard-locked to one API
• —Prompt engineering vs. fine-tuning vs. retrieval — which they're proposing and why it fits your specific accuracy and cost requirements, not just which one they're most comfortable building

Questions about data

What gets sent to a third-party model provider, where it's stored, and whether it's used to train that provider's future models are three different questions with three different answers depending on the provider and the contract terms — a partner who can't answer all three specifically, and just says "it's secure," hasn't actually checked. This matters more for GenAI than most software categories because the data often includes exactly the user content — messages, documents, images — that a privacy policy makes promises about.

Questions about evaluation

• —How will they know the feature actually works, beyond "it looked right in testing" — is there an evaluation set, a way to measure accuracy or hallucination rate over time
• —What's the plan for the cases where the model is confidently wrong — a fallback, a confidence threshold, a human-in-the-loop path, or nothing
• —How will quality regressions be caught after launch, when the underlying model itself gets silently updated by its provider

"It worked in the demo" is the easiest bar to clear and the least predictive of production reliability. A partner who brings up evaluation before you ask is a meaningfully different signal than one who only answers when pushed.

Questions about cost at scale

A GenAI feature that's free to prototype and expensive to run at real volume isn't a finished feature — it's a liability with a launch date. Ask about cost per user, not just cost per API call.

Per-call token costs are easy to quote and easy to misjudge at scale — the number that matters is projected cost per active user per month once usage is real, not the cost of the ten test prompts in a demo. Ask for that number, and ask what happens to margins if usage is 5x what's projected. If a partner hasn't modeled this, they haven't finished the estimate.

None of this is a reason to avoid GenAI — it's a reason to hire a partner who treats it as a distinct discipline with its own failure modes, not "regular software development plus an API call." That's the same lens we bring to our own AI/ML engagements, and to SaiyamAI, our own GenAI product — see how those questions play out in practice on the AI/ML engineers page, or bring us your specific idea directly.