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Practical notes on LLM evaluation, prompt versioning, and shipping language models to production.

LLM evals in CI: a minimal setup

You can start without a dedicated eval platform. This simple automated eval pipeline still catches real regressions before production.

Prompts are code. Source-file version control primitives apply here too, with a few key differences.

Most teams run evaluations, but fewer get results that predict production model behavior. Here is where they go wrong.

Staged rollouts, eval gates, and rollback workflows for language model features. A practical guide for first-time production teams.

A workflow that fits three prompts starts failing at fifty. Here is what to rethink first.

Vague rubrics yield vague scores. Write evaluation criteria that are specific and stable enough to repeat.

Hardcoding model calls feels quicker at first. It causes avoidable pain when you update a prompt or swap a model.

Every LLM project gathers undocumented scripts and copied utilities. Here is how that happens and how to address it before it becomes load-bearing.

Model updates and prompt changes can cause silent regressions. See what minimal regression detection requires and why teams skip it.

Our first year building an LLM dev tooling startup: what worked, what failed completely, and the developer conversation that changed our direction.

Why we chose ergonomics over terseness, how we handle async vs sync conventions, and decisions we would change today.

Evaluations differ. These properties separate one that guides decisions from one that produces a number your team ignores after week two.

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