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Generative AI Evaluation Framework

2 posts · Dec 2025 – Feb 2026

What this trail covers

This post details the evolution of Palantir's framework for evaluating generative AI systems, focusing on designing testing plans, defining 'good' outputs through syntax and semantics, and leveraging ground truth data. It emphasizes understanding end-to-end workflows and testable AI tasks, and outlines methods for curating evaluation data using benchmarks and domain expertise. This iteration likely refines the framework with new techniques or expands its applicability to more complex insurance underwriting scenarios.

All Palantir trails
  1. Evaluating Generative AI: A Field ManualThis post expands on the previous discussion of AIP Evals by detailing tradecraft for testing generative AI. It emphasizes analyzing end-to-end workflows and 'testable' AI tasks, defining 'good' outputs by evaluating syntax and semantics, and curating ground truth data. It introduces methods like using open-source benchmarks, LLM-as-a-Judge evaluators, and perturbation testing, highlighting their respective tradeoffs and use cases for robust AI system evaluation.
  2. MediumThis post details the evolution of Palantir's framework for evaluating generative AI systems, focusing on designing testing plans, defining 'good' outputs through syntax and semantics, and leveraging ground truth data. It emphasizes understanding end-to-end workflows and testable AI tasks, and outlines methods for curating evaluation data using benchmarks and domain expertise.