AI Safety, Evaluation and Cost Control

Build LLM applications that are safe, measurable and affordable: safeguards, evaluation metrics, monitoring and cost control, with runnable Python examples.

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Syllabus

Safety Foundations

1. What AI Safety Means for Applications
2. Common Failure Modes
3. Safety by Design: Layers

Input and Output Safeguards

1. Moderation and Thresholds
2. Handling Personal Data
3. Grounding and Hallucination Control
4. Refusal Calibration

Misuse and Abuse

1. Injection and Jailbreaks
2. Rate Limiting and Abuse Control

Fairness, Privacy and Accountability

1. Checking for Bias with Slices
2. Privacy and Data Governance
3. Documentation, Transparency and Human Oversight

Evaluation

1. Building an Evaluation Set
2. Metrics: Accuracy, Precision, Recall, F1
3. How Sure Is Your Score?
4. LLM Judges and Human Agreement
5. A/B Tests and Regression Checks

Monitoring in Production

1. What to Log and Watch
2. Latency Percentiles and Alerts

Cost Control

1. Token Economics
2. Model Routing and Cascades
3. Caching, Batching and Trimming
4. Budgets, Quotas and FinOps for AI

Putting It Together

1. Case Study: Launching a Support Assistant
2. Revision: Cheat Sheet and Self-Check