Shipping AI Features with the Claude API
Published 20 May 2026 1 min readSotatek Korea
A prototype that calls an LLM is easy; a reliable feature is not. A pragmatic checklist for taking AI from demo to production — prompts, tool use, streaming, cost and evaluation.
Adding an AI feature looks simple in a demo: send a prompt, render the reply. Shipping one clients trust is a different job. The gap is everything around the model call — latency, safety, cost, and knowing whether the output is actually good.
Design for streaming from day one
A three-second wait for a full response feels broken; the same three seconds streamed token-by-token feels fast. Build the UI around a stream, show a typing indicator, and let users stop generation. Perceived speed is a feature.
Treat user input as untrusted
Anything a user can type can try to hijack your prompt. Two cheap defenses go a long way:
- Keep instructions in the system prompt, never concatenated with raw user text.
- Constrain outputs with tool use / structured schemas so a stray instruction cannot change the response shape.
Control cost before it controls you
- Cache stable system prompts and long context so you are not re-billed for the same tokens.
- Pick the smallest model that passes your eval — escalate only when it fails.
- Cap output length; most features do not need unlimited tokens.
You cannot improve what you do not measure
Before launch, assemble a small set of real inputs with expected outcomes — even 20 cases. Run them on every prompt change. It turns "it feels worse" into a number, and that number is what lets you ship changes with confidence.
The model is the easy part. The harness around it — streaming, guards, caching, evals — is the product.
