LLM demos are easy. LLM products are not. The difference is evaluation, cost control, privacy, and a plan for when the model is wrong.
Start with a job and a failure mode
Write:
- The user job the feature assists
- What “good” looks like in measurable terms
- What happens when the model fails (fallback, human review, safe refusal)
If you cannot describe failure, you are not ready to ship.
Prefer product engineering over model worship
Most business features succeed with strong product engineering around a capable model API:
- Tight tool/context boundaries
- Logging that respects privacy
- Rate limits and budget alarms
- Versioned prompts and configs
Train custom models only when ROI is clear.
Evaluate before you celebrate
Build a small evaluation set from real (sanitized) examples. Track regressions when prompts or models change. “It looked good in the meeting” is not a quality bar.
Operability is part of the feature
Treat AI paths like any other production dependency: timeouts, retries with care, dashboards, and ownership. Our artificial intelligence and API development practices assume AI is a product surface — not a slide.
