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The core craft of measuring LLM performance: metric design, test data generation, LLM-as-a-judge methods, evaluation frameworks, and how to translate model behavior into business confidence.
Most teams building LLM evaluation pipelines spend a lot of time on the judge itself, which model to use, how to write the rubric, and which dimensions to score. Almost none of that effort goes into evaluating whether the judge is actually right.
Three approaches to test-case generation for GenAI systems — red teaming with curated attack databases, gold-standard generation for RAG and tool calling, and synthetic-user simulation for multi-turn conversations. With concrete examples for each.
Benchmarks for Glider, FlowJudge, Phi-3.5-mini, Selene, GPT-4o, and Claude 3.5 Sonnet as judges across general rubrics and red-team safety tasks. Where small fine-tuned models hold up, where they don't, and what the latency and memory tradeoffs look like.
Most enterprise GenAI projects stall when they try to scale past the MVP. The reason is evaluation strategy. Here's the four-stage view of where teams get stuck, and what it takes to move past Stage 3.
