Ship safer, cheaper AI
Short, tactical posts on guardrails, cost control, and on-call for LLM products.
Shadow evaluations: compare prompts/models on real traffic without risking users
Offline evals are never enough. Shadow mode runs a new prompt/model alongside production (without showing it) so you can measure cost, latency, and quality on real traffic before a rollout.
Production RAG observability: a retrieval health playbook
RAG improves answers by injecting external context, but most production failures come from retrieval. This playbook shows what to log, which signals catch regressions early, and how to fix issues fast.
Shipping prompt changes without surprise regressions
Prompt edits are the fastest way to ship value—and the fastest way to break production. Here’s a release workflow (versions, canaries, rollbacks) that makes prompt changes boring again.
LLM guardrails that don’t break shipping velocity
Guardrails shouldn’t be a governance program. Here’s a practical setup—budgets, explainable checks, and a tight review loop—that makes LLM features safer without slowing shipping.
Cutting LLM costs without hurting quality
Cost work goes wrong when it’s just “change the prompt.” This playbook starts with measurement at the route level, then uses call volume, context, and model choice to cut spend without turning quality into guesswork.
Incident runbooks for LLM products
A practical runbook for LLM incidents—quality regressions, latency/cost spikes, and provider errors—with the exact signals you’ll wish you had when the pager goes off.