A generative AI demo is easy to build. Point a chat interface at an LLM API, write a decent system prompt, and it looks impressive in about a day. Getting that same feature to hold up in production, answering real questions from real users against your real data, is a different engineering problem, and it's where most of our work on these projects actually happens.
Retrieval quality, not model choice, is usually the bottleneck
The model itself is rarely the weak link. What determines whether a RAG (retrieval-augmented generation) system gives good answers is whether it retrieves the right context from your documents before generating a response. Bad chunking, poor embedding choices, or a document store that doesn't reflect how your content is actually structured will produce confidently wrong answers regardless of which underlying model you're using.
This is unglamorous work: cleaning source documents, deciding how to chunk them, tuning retrieval to actually surface the relevant passage instead of a superficially similar one. It doesn't demo well. It's also most of what makes the difference between a system that's useful and one that hallucinates.
Hallucination doesn't go away, it gets managed
We don't tell clients a generative AI feature will never make something up. Any LLM can hallucinate. What we design for is reducing that risk and controlling what happens when confidence is low: grounding answers in retrieved source documents, scoping clearly what the system will and won't attempt to answer, and building fallback behavior, "I don't have enough information to answer that confidently" beats a fluent, wrong answer every time.
Evaluation has to be systematic, not vibes-based
Testing a prompt by trying it a few times and seeing if it looks right doesn't scale, and it doesn't catch regressions when you change the prompt or swap models later. We build evaluation sets: real questions with known-good answers, checked systematically before a prompt or retrieval change ships, not just eyeballed once before launch.
The parts that don't show up in a demo
- Guardrails on agentic actions. If the system can call tools or take multi-step actions, what's it allowed to do without a human confirming first.
- Cost modeling. Every request has a real, recurring cost to the model provider. We estimate that against expected volume before launch, not after the first bill.
- Monitoring for drift. Model providers update their models. Retrieval quality degrades as your document base grows stale. Both need to be watched, not assumed static.
None of this is what makes for an exciting five-minute demo. It's what makes the difference between a generative AI feature your users trust and one that quietly erodes that trust the first time it confidently gets something wrong.