Shipping LLM features without the chaos

Shipping a feature powered by a large language model is deceptively easy to start and surprisingly hard to finish. You can prototype something impressive in an afternoon. Getting it to behave predictably enough to put in front of real users — and keep it that way — is a different problem entirely.

The single biggest lever we've found is evaluation. Before writing a line of integration code, define what 'good' looks like. Build a small dataset of inputs and expected outputs, then score every prompt change against it. This sounds tedious until the first time it catches a regression you'd have missed completely.

Guardrails come in two flavours: input validation and output validation. Input guardrails filter obvious abuse before it reaches your model call. Output guardrails check that the response actually answers the question, stays within scope, and doesn't hallucinate facts you care about. We run both — the cost is negligible compared to a single bad response that ends up screenshotted on social media.

Model selection deserves more thought than most teams give it. A smaller, fine-tuned model often outperforms a frontier model on a narrow task, costs less per token, and has lower latency. We evaluate at least two candidates per feature, using our eval suite as the tiebreaker.

Prompt versioning is non-negotiable. Treat prompts as code: version them, review them, and never change a production prompt without running the full eval suite first. We store prompts alongside the application code so they travel through the same deployment pipeline and can be rolled back just as easily.