Practical Generative AI: From Prototype to Production

A prototype is judged by a handful of impressive answers. A product is judged by the worst 1% of interactions—at 2 a.m.—when a customer is frustrated and your logs are empty.

Production LLM features fail for familiar reasons: missing guardrails, unclear data boundaries, no evaluation strategy, and “it worked on my prompt” optimism. The good news: you can design around all of these with a small set of patterns.

• Define the job-to-be-done, not “chat with our docs”.
• Build feedback loops before you build UI polish.
• Treat prompts and retrieval as deployable assets with versioning.

If you only do one thing before launch, do this: decide what “good” means, then make it measurable. Your model will drift, your docs will change, and user intent will surprise you.

• Evaluation: golden set + regression tests for top user tasks.
• Observability: trace prompts, retrieved sources, latency, and errors.
• Safety: input/output filtering + refusal strategy + escalation path.
• Cost controls: caching, token budgets, and rate limits.
• Human override: a way to correct answers and update source-of-truth.

Users trust systems that explain boundaries. Even a perfect answer feels risky if it arrives with zero context. “Show your work” doesn’t mean dumping raw citations—it means making the reasoning inspectable.

In practice: display sources, summarize key evidence, and be explicit about unknowns. That small UX shift reduces support tickets and makes failures recoverable.

Don’t launch to everyone on day one. Start with a controlled audience, use feature flags, and ship quality improvements weekly. The best LLM products look less like “big bang releases” and more like fast, disciplined iteration.

• Week 1–2: baseline MVP + logging + golden set.
• Week 3: retrieval tuning + guardrails + failure UX.
• Week 4: monitoring dashboards + cost controls + internal beta.