🏁 CHECKPOINT PROJECT

This checkpoint assessment is here to convert those hours of operational and evaluation knowledge into evidence of production-level engineering judgment.

Passing this checkpoint means you will have verified all of the following:

• You can design evaluation systems for RAG pipelines and agentic workflows rather than relying on subjective prompt testing.

• You can instrument, monitor, and trace AI systems with tools like LangSmith and reason about reliability, latency, failure patterns, and production behavior.

• You can evaluate and improve hallucination behavior through grounded retrieval, validation patterns, and evaluation-driven iteration.

• You can reason about ML/LLMOps concepts including deployment lifecycle management, observability, monitoring, and operational scalability.

• You can implement data quality validation workflows and understand how bad data propagates into unreliable AI system behavior.

• You understand how production AI systems are monitored, evaluated, debugged, and maintained after deployment rather than treating deployment as the end of the engineering process.

• You can defend the operational decisions behind an AI system, including how quality is measured, how failures are detected, and how reliability is maintained over time.

Please download the full Ops & Evaluation Checkpoint Assessment Document below in order to begin! 👇