Generative AI systems evolve constantly, and the impact of prompt or model changes often isnt clear until real users interact with them in production. In this session, learn how teams using Amazon Bedrock safely experiment with AI at runtime, testing models and prompts with targeted rollouts, evaluating system outputs online, and optimising against real business result
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Treat prompts and models as feature flags. Ramp them, A/B them, kill-switch them. Don't ship a model change to 100% of users at once.
Why it matters
Model upgrades are silent breaking changes. Without runtime control, every prompt tweak is a high-blast-radius deployment.
The hard parts
Evaluating LLM outputs in production is harder than for traditional features. Click-through doesn't capture "the answer was confidently wrong." You need offline evals + online signals together.
Playbook moves
(1) Tag every prompt change with a flag. Make rollback a one-click operation. (2) Define explicit output evaluators: faithfulness, toxicity, latency, cost-per-call. (3) Roll to 1% before 100%. Always.
The surprise
The best leading indicator of a bad prompt change isn't user satisfaction — it's the *variance* of agent token consumption. Misaligned prompts cause agents to retry, second-guess, and burn tokens. Track token-spend variance per session; spikes there precede user complaints by hours.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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