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structured-evaluation

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ksm26 / Evaluating-AI-Agents

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A hands-on course repository for Evaluating AI Agents, created with Arize AI, that teaches you how to systematically evaluate, debug, and improve AI agents using observability tools, structured experiments, and reliable metrics. Learn production-grade techniques to enhance agent performance during development and after deployment.

structured-evaluation ai-agents ai-in-production ai-observability prompt-engineering llmdevops llm-evaluation debugging-and-testing ai-experiment opensource-ai agent-observability
  • Updated May 12, 2025
  • Jupyter Notebook

chigwell / optimistic-pessimistic-evaluator

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A new package is designed to facilitate structured evaluation of a system's performance comparison between optimistic and pessimistic approaches. It takes a textual description or data snippet as inpu

pattern-matching structured-evaluation automated-analysis performance-comparison retry-logic text-based-interface structured-prompts textual-input optimistic-vs-pessimistic language-model-assessment results-reporting response-conformity reliable-insights validation-strategies systematic-comparison trade-offs-analysis performance-trade-offs data-snippet-processing criteria-based-evaluation
  • Updated Dec 21, 2025
  • Python

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