Shipping AI Features to Production: A Practical Checklist

A working demo is the easy 20%. The gap between "the model gave a great answer in our notebook" and "thousands of customers depend on this every day" is where most AI projects quietly stall. The prototype impressed everyone; the production version never shipped.

The reason is rarely the model. It's everything around the model — evaluation, guardrails, latency, cost, and observability — that a demo lets you skip and production does not. Below is the checklist we work through before we call an AI feature production-ready.

1. Define what "correct" means before you build

You cannot ship what you cannot measure. Vague goals like "the chatbot should be helpful" can't be tested, so quality silently regresses with every prompt change.

• Write down 30–100 real example inputs with the outputs you'd accept.
• Decide the metric: exact match, a rubric scored by a stronger model, retrieval hit rate, or human review.
• Make this an automated eval you can run on every change — the equivalent of a test suite for non-deterministic systems.

If you only do one thing from this list, do this. Everything else builds on it.

2. Engineer for failure, not just the happy path

LLMs fail differently from normal code: they produce confident, well-formatted, wrong answers. Plan for it.

• Validate outputs structurally. If you expect JSON, parse and schema-check it; retry or repair on failure.
• Set a fallback. What happens when the model is down, slow, or returns nonsense? A degraded-but-safe path beats a 500 error.
• Constrain the blast radius. An agent that can take actions needs allow-lists, confirmation steps for irreversible operations, and hard limits on loops.

3. Add guardrails on both sides

• Input: strip or neutralize prompt-injection attempts, especially when user content or retrieved documents flow into the prompt.
• Output: check for leaked secrets, PII, or off-policy content before it reaches the user.

Guardrails are not optional polish — they're the difference between a feature and an incident.

4. Make latency and cost first-class requirements

A response that's correct but takes 30 seconds, or costs more than the revenue it generates, is not production-ready.

• Set a latency budget and measure p95, not just the average.
• Stream responses where the UX allows it — perceived speed matters as much as real speed.
• Track cost per request. Cache aggressively (embeddings, repeated prompts). Right-size the model: the biggest model is rarely the correct default.

5. Instrument everything

You can't improve what you can't see. Log the full picture for every request: input, retrieved context, final prompt, model output, latency, token cost, and the eval verdict where available.

This turns "users say it's sometimes wrong" — an unfixable complaint — into "these 14 queries failed retrieval," which is a task.

6. Close the loop after launch

Production is where the real distribution of inputs shows up. Capture user feedback (even a thumbs up/down), review failures weekly, and feed the hard cases back into your eval set. An AI feature is a system you operate, not a project you finish.

The short version

Ship an AI feature only when you can answer: How do we measure correctness? What happens when it fails? What stops it leaking or being manipulated? Is it fast and cheap enough? Can we see what it did? And how do we catch regressions next week?

If those answers are solid, you have a product. If they're missing, you have a demo — and the services we build exist to close exactly that gap. If your AI is stuck between the two, tell us where it's stuck and we'll review it.