AI cost control in practice: Tokens, models and run budgets

In this class

What this class covers

1. 01Match models to task difficulty and reserve costly models for critical judgments
2. 02Manage context and tool calls to control task execution costs
3. 03Limit retries and agent steps to bound task execution
4. 04Distinguish per-task, daily, and per-user budget caps and stopping mechanisms

Instructor

Learn with Wisely Chen

Wisely Chen

Wisely Chen

AI Startup Executive

• Over 20 years of experience in the IT industry, including roles as a Google Cloud consultant and enterprise Data & AI lead
• Currently an executive at an AI startup, focused on enterprise AI architecture
• Drives AI agents, on-premise LLMs, workflow automation, and digital transformation in traditional industries

Workflow · 5 classes

5 courses in Workflow

One price covers all 5 classes. Start with the one you want.

1. 01 AI's two limits: Too much to read, different every timeEric Wu · About 30 min →
2. 02 How to check AI output: Human, AI and independent adversarial checksPeggy Lo · About 25 min →
3. 03 Working with AI like an engineer: Specs, task breakdown and controlled deliveryRichard Lee · About 20 min →
4. 04 AI cost control in practice: Tokens, models and run budgetsWisely Chen · About 30 min This course
5. 05 Sort workflow steps: Break down a customer-support flowEric Wu · About 40 min →