AI Engineer Portal
Your personal operating system for career transition.
Evaluation and Observability
Measure behavior before scaling usage and complexity.
Evaluation metrics for LLM outputs
Choose the right metric for the right failure mode: accuracy, faithfulness, relevance, toxicity, and when LLM-as-judge beats deterministic scoring.
Evaluation metrics for LLM outputs
Choose the right metric for the right failure mode: accuracy, faithfulness, relevance, toxicity, and when LLM-as-judge beats deterministic scoring.
Building eval harnesses
Construct golden datasets, regression test suites, and A/B evaluation pipelines that make prompt changes safe to ship.
Tracing and logging AI requests
Build structured traces that let you debug bad outputs, measure latency, and audit token spend without leaking sensitive data.
Production monitoring for AI features
Build dashboards, alerts, and drift detection that catch quality degradation before users do.
Human-in-the-loop evaluation
Design annotation workflows, measure inter-rater reliability, and close the feedback loop between human judgment and prompt improvement.
Building eval harnesses from scratch
Architect a minimal, production-grade eval harness with pluggable scorers, structured reporting, and clear separation between test cases, runners, and judges.
Prompt regression testing
Prevent prompt changes from silently breaking existing behavior using golden datasets, automated comparison, and CI integration.