Last commit on August 16, 2026·Created on June 23, 2026
ai-evals-course/evals-skills
“A framework for building and validating product-specific AI evaluation pipelines”
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TL;DR
This collection provides a structured approach to measuring AI performance, moving from synthetic data generation to the creation of custom judge prompts. It helps engineers transition from anecdotal testing to systematic evaluation by establishing rigorous validation for the evaluators themselves.
The skills focus on the end-to-end lifecycle of quality assurance, including specialized techniques for RAG systems and the development of annotation interfaces to organize failure modes through error discovery.
WHO IT'S FOR
AI / ML Engineers
build product-specific AI evaluation pipelines
LLM Application Developers
evaluate and optimize RAG systems
AI Product Managers
perform error analysis and failure mode discovery
Data Labeling Leads
create custom human-in-the-loop annotation tools
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