From Demo to Dependable: Shipping AI Features That Hold Up
A prototype that impresses in a meeting is the easy part. Here is the work that turns an LLM demo into a feature your customers can rely on.
Saji Nammari3 min read
Most AI projects we're asked to rescue share a story. Someone wired a large language model to a prompt, tried it on a handful of examples, and the room lit up. Weeks later the feature still isn't in production. Or it is, and support tickets are piling up because it answers confidently and wrongly.
The demo wasn't a lie. It just answered a different question. A demo asks can this work? A product asks does this work, every time, for everyone, at a cost we can carry? Closing that gap is most of an AI engineer's job.
Define the job before choosing the model
Write down, in one sentence, what the feature does for the user and what a good answer looks like. "Summarize support tickets" is a topic. "Give the on-call agent a three-line summary that names the customer, the problem, and what was already tried" is a job. You can test a job. You can't test a topic.
Collect real inputs early
Demos run on inputs the builder chose. Production runs on whatever users type, paste, and upload. Before building further, gather a few dozen real examples, including the messy ones: long threads, mixed languages, empty fields, attachments. They become your first test set, and they will change your design.
Make failure visible and cheap
- Constrain the output. Ask for structured output and validate it. A response that doesn't parse should fail loudly, not render as garbage.
- Give the model a way to say no. "I don't have enough information" is a valid answer. Design the interface to handle it gracefully.
- Keep a person in the loop where the stakes are high. Drafts that someone approves are often worth more than automation that occasionally embarrasses you.
Measure, then iterate
Every prompt change, model swap, or retrieval tweak should run against the same evaluation set before it ships. Without that, you're tuning by feel, and a fix in one place quietly breaks another. We wrote more about this in Write the Evals First.
Plan for cost and latency on day one
A model that takes eight seconds feels broken in a chat box and perfectly fine in a nightly report. Decide where the feature lives in the user's flow, then pick the smallest model that meets the quality bar. Cache what you can, stream what you can't, and set spending alerts before the first invoice surprises you.
Ship small, watch closely
Roll out to a slice of users, log inputs and outputs with privacy controls in place, and review a sample every week. The first month in production teaches more than any amount of prompt polishing in a notebook.
Give it an owner after launch
Traditional features mostly stay done once they ship. AI features drift. Providers update models, users find new ways to phrase things, and the documents behind the answers change. Someone on the team should own the feature's quality the way someone owns uptime: watching the evaluation scores, reading flagged responses, and deciding when a model upgrade is worth taking.
None of this is glamorous. It's the difference between AI as a slide in a pitch deck and AI as a feature people trust, and it's the work our AI engineers do inside client teams.
About the author
Saji Nammari
Founder & Chief Executive Officer
Saji serves as the Founder and Chief Executive Officer, having successfully pivoted from five years as a highly effective freelance software and data professional. His leadership is rooted in the pragmatic, results-driven mentality required to deliver all types of projects, driving a culture of agility and technical rigor. Outside of work, Saji enjoys the creative disciplines of literature, music, and the satisfaction of pure problem solving.
