Evals & Observability
Build evaluation pipelines, production monitoring, and feedback loops that keep AI systems reliable and improving.
Why take this course?
The difference between a demo and a product is knowing when it breaks. This course teaches you to evaluate, monitor, and continuously improve AI systems in production.
Prerequisites
This course builds on concepts from the following courses. It is recommended to complete them first:
Course Modules
Evals separate "vibe-checked" AI from production AI. Learn automated metrics (BLEU, ROUGE, exact match), LLM-as-judge scoring, human evaluation, and when to use each approach.
Learning Goals
- Compare automated metrics, LLM-as-judge, and human evaluation approaches.
- Design task-specific evaluation rubrics for classification, generation, and retrieval.
- Understand RAG-specific evals: faithfulness, relevancy, context precision, context recall.
Evals are only useful if they run automatically. Build CI-integrated evaluation pipelines with regression gates, A/B testing frameworks, and dataset management for continuous quality assurance.
Learning Goals
- Build automated eval pipelines that run on every prompt or model change.
- Implement regression gates that block deploys on quality drops.
- Design A/B testing frameworks for comparing model configurations.
You cannot improve what you cannot see. Learn LLM-specific tracing, span-level logging, cost tracking, latency monitoring, and the dashboards that keep AI systems healthy in production.
Learning Goals
- Implement LLM-specific tracing with spans for each step (retrieval, generation, tool calls).
- Set up cost and latency monitoring with actionable alerts.
- Design logging strategies that capture enough context without leaking PII.
Production data is your best training signal. Learn to build feedback loops, data flywheels, prompt versioning systems, and iterative improvement cycles that make your AI system better over time.
Learning Goals
- Design feedback loops that turn user interactions into improvement signals.
- Implement prompt versioning and rollback strategies for safe iteration.
- Build data flywheels that systematically improve model quality from production usage.