This session is about replacing “AI iteration by gut feel” with a playbook that scales across teams and releases. Models, prompts, and best practices move fast, and without a repeatable approach, experiment history gets scattered, decisions become hard to defend, and teams miss edge cases that only show up in production. We’ll outline an evaluation and iteration loop that works from early prototyping through launch and beyond, anchored in LLM Observability. That includes capturing real execution traces, turning production behavior into reusable evaluation datasets, and comparing prompt and model variants with structured experiments that measure what matters to your users and business. We’ll also cover how to keep the loop healthy after deployment: detecting drift, catching regressions in cost or latency, and building safeguards that reduce operational risk as usage grows. Attendees will leave able to design an evaluation strategy, run faster investigations when outputs look wrong, and ship changes with clearer confidence. This aligns with the Developer Autonomy theme by giving engineers a practical path to move quickly without breaking trust.
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