Evaluation & Monitoring

Evaluation determines how well an agent performs (correctness, helpfulness, safety), usually on a test dataset. Monitoring determines how the system is running (latency, errors, cost) in a live environment. Both are essential for the lifecycle management of AI systems.

When to Use

• CI/CD: Rejecting code changes if they drop accuracy below a threshold.
• A/B Testing: Comparing Prompt A vs. Prompt B to see which users prefer.
• Cost Auditing: Understanding which agents or tools are driving up the bill.
• Drift Detection: Noticing if the model starts hallucinating more often on new data.

Use Cases

• LLM-as-a-Judge: Using GPT-4 to grade the answers of a smaller model.
• Latency Tracking: Measuring the time-to-first-token (TTFT) and total generation time.
• Topic Clustering: Analyzing user queries to see what topics are trending or failing.

Implementation Pattern