Cost Controls: Token Limits and a Budget Note You Can Audit
Explore how to enforce max output token limits to prevent runaway AI responses and maintain reliable, compact JSON outputs. Understand how to create auditable budget notes that track token usage and execution health, making cost control a structured practice in AI workflows. Learn to set conservative defaults and monitor step ceilings for predictable, cost-effective processing.
When an automated triage step is tasked with returning a tight JSON payload, runaway model outputs can quietly destroy your pipeline's performance. A response that rambles into unprompted advice or extra commentary doesn't just clutter your audit logs—it inflates token bills and adds unnecessary latency. To keep your workflow fast and cost-effective, you need hard generation limits paired with auditable run records. In this lesson, we will configure output ceilings and construct structured budget notes to track cost and execution health across every execution.