This guide is the conversation a friend in the industry would have with you over coffee — not a sales pitch, not a checklist copied from a template, but the actual questions worth asking before you sign anything.
Why This Decision Is Riskier Than It Looks
Hiring a web developer who does mediocre work usually costs you a clunky website. Hiring an LLM development partner who does mediocre work can cost you something much harder to walk back: a product that hallucinates false information to your customers, leaks sensitive data through a poorly secured integration, or racks up an API bill nobody budgeted for because nobody thought to optimize token usage.
The stakes are higher because the failure modes are less visible upfront. A broken button is obvious the moment you click it. A chatbot that occasionally invents a return policy that doesn't exist, or confidently tells a customer something factually wrong, can run for weeks before anyone notices — and by then it's a trust problem, not just a bug ticket.
None of this means you should be scared of building with LLMs. It means the vetting process deserves more rigor than most business owners initially expect to put into it.
The Portfolio Test: Look Past the Demo
Almost every company you talk to will show you an impressive demo. Demos are cheap to make impressive — a curated set of test questions, a controlled environment, no messy real-world data. What you actually want to know is much less glamorous and much more revealing:
Ask to see something that's been in production for at least six months. Not a proof of concept. Not a pilot. A system that's been handling real users, real edge cases, and real support tickets long enough for the rough edges to show up. Any team that's genuinely good at this work will have at least one example they're proud to walk you through in detail — including the parts that didn't go smoothly at first.
Ask what broke, and how they fixed it. This is the single most revealing question in the entire vetting process. Teams with real production experience will have a specific, honest answer — a hallucination problem they solved with better retrieval grounding, a latency issue they fixed with caching, a cost overrun they caught and corrected. Teams without real experience will either dodge the question or give you an answer so polished it sounds rehearsed, because it probably is.
Ask who on the team actually wrote the code, not just who was on the sales call. It's astonishingly common for the person pitching you to have never touched the codebase, while the actual engineering gets handed off to a subcontracted team you never meet and never get to evaluate.
The Technical Questions That Actually Separate Serious Teams From the Rest
You don't need to become an AI engineer to ask good technical questions — you just need to know which questions expose real expertise versus buzzword fluency.
"How do you handle hallucinations?"
There's no such thing as a company that's "solved" hallucinations — anyone who claims that is telling you something important about their honesty, not their technology. What you want to hear instead is a specific, layered approach: retrieval-augmented generation grounded in your actual data, confidence scoring that flags uncertain answers, human review workflows for high-stakes outputs, and clear fallback behavior when the model genuinely doesn't know something. A vague "we fine-tune the model to be more accurate" is a sign they haven't actually grappled with this problem in production.
"What happens to our data?"
This question matters more than most business owners initially realize. Does your data get used to train or fine-tune models that other clients might benefit from? Is it sent to a third-party API, and if so, under what data retention terms? Can the system run on-premises or in a private cloud if your industry requires it? A serious partner will have clear, specific answers — not reassurances that dodge the specifics. If your business handles healthcare, financial, or other regulated data, this isn't a nice-to-have question; it's the question.
"What's your plan for when the underlying model changes?"
This one catches people off guard, but it's critical. The model your product launches on will not be the model your product runs on in eighteen months — providers update, deprecate, and retire models constantly, and prompts that work perfectly on one version can behave differently on the next. Ask how the team architects around this. Do they abstract the model layer so it's not painful to swap providers? Do they have a regression-testing process for when a model updates? Teams that haven't thought about this will build you something that works great on day one and quietly degrades a year later.
"How do you actually measure whether this is working?"
If the answer is "user feedback" and nothing else, that's a red flag. Serious teams track concrete metrics: resolution rate, hallucination rate on sampled outputs, latency under real load, cost per interaction, and escalation quality when the system hands off to a human. If a team can't tell you how they'll prove the system is working three months after launch, they haven't actually planned for what happens after launch — which, not coincidentally, is when most of the real value (or real damage) happens.
The Pricing Conversation Nobody Enjoys, But Everybody Needs
LLM development pricing tends to come in three flavors, and each one tells you something about the incentives at play.
Fixed-price, fixed-scope sounds appealingly simple, but LLM projects rarely have genuinely fixed scope — requirements shift as soon as you see the system working with real data, which is almost always. Fixed-price arrangements can create an incentive for a vendor to resist necessary changes rather than accommodate them, because every change eats into their margin.
Time and materials is more flexible but requires real trust, since you're paying for hours regardless of outcome. This works best with a team that has a track record you can verify — see the portfolio test above — and a clear reporting rhythm so you're not surprised by the invoice.
Hybrid models — a fixed scope for a well-defined MVP, followed by time-and-materials for the iteration that inevitably follows — tend to be the most honest reflection of how these projects actually go. Be skeptical of anyone who insists a complex, custom LLM system can be fully scoped and fixed-priced from day one; it's either a sign of inexperience or a sign they're planning to pad the estimate heavily to protect themselves from the inevitable scope creep.
Whatever the model, ask about ongoing costs separately from development costs. API usage, hosting, monitoring, and maintenance are recurring expenses that many business owners don't fully budget for upfront — and a vendor who doesn't proactively walk you through this is either inexperienced or hoping you won't ask.
Red Flags Worth Walking Away From
A few patterns come up often enough among vendors that burned other business owners that they're worth naming directly:
- They can't explain their approach without saying "proprietary AI" as if that ends the conversation. Real teams can explain their architecture in plain language. Vagueness dressed up as secrecy is usually just vagueness.
- They promise zero hallucinations, perfect accuracy, or a fully autonomous system with no human oversight. These promises reflect either dishonesty or a lack of hands-on production experience — often both.
- They can't name a single thing that went wrong on a past project. Everyone who's shipped real LLM systems has a story about something that broke. The absence of one is more concerning than the presence of one.
- The contract has no clear post-launch support terms. LLM systems need ongoing tuning as usage patterns emerge and models change. A contract that ends at deployment is setting you up to either scramble for a new vendor or watch quality quietly degrade.
- Nobody asks you hard questions back. A good development partner should be asking about your data quality, your compliance requirements, your existing systems, and your actual use case in detail before ever quoting a price. If the sales conversation is entirely one-directional, that's telling you something about how the actual engineering relationship will go.
The Question That Ties Everything Together
If there's one question to end every vendor conversation with, it's this: "Walk me through exactly what happens in the first month after this launches." The answer will tell you almost everything you need to know. A team with real experience will talk about monitoring dashboards, feedback loops, a plan for reviewing flagged outputs, and a process for iterating based on what real usage reveals. A team without that experience will talk mostly about the launch itself, because that's where their thinking stops.
The Bottom Line
Choosing an LLM development partner isn't fundamentally different from choosing any serious technical partner — you're looking for evidence over promises, specifics over buzzwords, and a track record you can actually verify rather than take on faith. The market is currently full of companies that learned the vocabulary faster than they learned the craft. The businesses that end up genuinely happy with what they built are, almost without exception, the ones that slowed down at the vetting stage long enough to ask the uncomfortable questions — and picked a partner willing to answer them honestly, including the parts that weren't flattering.
The extra week you spend vetting properly is cheap compared to the six months you'll spend rebuilding a system that was rushed into production by a team that oversold what they could actually deliver.
