Run Fewer LLM Evals with Smart Sampling: Catch Regressions (python)

Professor Py: AI Engineering · Intermediate ·🧠 Large Language Models ·2mo ago

About this lesson

Targeted sampling in Python to catch regressions: build a drop-driven sampling pattern that finds LLM regressions with far fewer tests. Implement a small prompt catalog, record a baseline, compute drop-driven risk scores, and prioritize checks using Python (itertools) to save time, budget, and speed up CI/monitoring. For practical AI engineering and reproducible LLM evaluation patterns, subscribe for more tutorials from Professor Py. #Python #LLM #AIEngineering #ModelMonitoring #CI #Testing #MachineLearning

Original Description

Targeted sampling in Python to catch regressions: build a drop-driven sampling pattern that finds LLM regressions with far fewer tests. Implement a small prompt catalog, record a baseline, compute drop-driven risk scores, and prioritize checks using Python (itertools) to save time, budget, and speed up CI/monitoring. For practical AI engineering and reproducible LLM evaluation patterns, subscribe for more tutorials from Professor Py. #Python #LLM #AIEngineering #ModelMonitoring #CI #Testing #MachineLearning
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