Building AI Features That Actually Ship

Most AI features die in staging. They work in the demo, impress the founder, then hit three months of edge-case wrangling before quietly being cut. Here is what I have learned building AI features that actually reach users.

Start narrow, not smart

The instinct is to build something impressive. Build something useful instead. A single, well-scoped LLM call that solves one real user pain is worth more than a multi-agent system that solves five hypothetical ones.

Good scope: summarize a document the user just uploaded. Bad scope: understand all user intent across the product.

Treat the LLM like an unreliable API

Because it is. Your architecture should assume the model will sometimes return garbage, timeout, or hallucinate. That means:

• Always validate structure — use Zod or equivalent on every AI response
• Set hard timeouts — 10-15s max for user-facing calls
• Fallback paths must exist before the feature ships
• Log every call with input, output, latency, and cost

Prompt engineering is product design

Writing a good system prompt is closer to writing good documentation than to writing code. It needs to be:

• Specific about format — tell the model exactly what to return
• Explicit about failure cases — what to say when it cannot answer
• Version-controlled — treat prompts like code, review them like PRs

The shipping checklist

Before any AI feature goes to production, I run through:

• Does the fallback UI look good when the AI call fails?
• Is the response streamed if it takes more than 2 seconds?
• Are we logging enough to debug production failures?
• Did we test with real user data, not curated examples?

AI features that ship are not the most impressive ones. They are the most reliable ones. Scope small, test hard, ship fast, and iterate with real users.