Record the tool call id, the arguments before and after your code touches them, the raw result, and the message the next model request actually sent, so the trace can still explain the answer a week later.
Why LLM rate limits are product decisions about who waits, who fails, and who gets a degraded answer, and how to design them that way.
Why LLM golden sets go stale on model upgrades, how to tell product regressions from scorer drift, and how to re-anchor the suite as part of the upgrade.
Treat prompts as named, immutable artifacts with rollout, rollback, and ownership so production behavior is explainable.
How to attach dollars to products and features so a month-end inference spike is a query, not a scavenger hunt.
A guest post from the Click2Login team on the operating disciplines that auth migrations and LLM rollouts share, and what teams running one can borrow from the other.
How to set up evaluation for an LLM feature so the team can ship changes with confidence, including what to test, what to ignore, and what the operational cadence looks like.
How to recognize the moment a team's LLM work has outgrown prompt engineering and needs the operational layer that turns experimental features into reliable production.