Building an LLM evaluation framework: best practices | Datadog
Explore best practices for building an evaluation framework for production LLM applications.
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Explore best practices for building an evaluation framework for production LLM applications.
Spark-native LLM evaluation framework with confidence intervals, significance testing, and Databricks integration - bassrehab/spark-llm-eval
OpenAI introduces FrontierScience, a benchmark testing AI reasoning in physics, chemistry, and biology to measure progress toward real scientific research.
OpenAI introduces a real-world evaluation framework to measure how AI can accelerate biological research in the wet lab. Using GPT-5 to optimize a molecular cloning protocol, the work explores both the promise and risks of AI-assisted experimentation.
Polish emerges as top language in multilingual AI benchmark testing PPC Land
OpenAI’s new research explains why language models hallucinate. The findings show how improved evaluations can enhance AI reliability, honesty, and safety.
Explore Stax, an experimental developer tool that streamlines LLM evaluation with human labelling and scalable LLM-as-a-judge auto-raters for data driven decisions.
OpenAI and Anthropic share findings from a first-of-its-kind joint safety evaluation, testing each other’s models for misalignment, instruction following, hallucinations, jailbreaking, and more—highlighting progress, challenges, and the value of cross-lab...
Next generation LLM evaluation framework powered by Vitest.
MLPerf Client 1.0 AI benchmark released — new testing toolkit sports a GUI, covers more models and tasks, and supports more hardware acceleration paths Tom's Hardware
Why pre-deployment testing is not an adequate framework for AI risk management
We measured the performance of GPT-4o given a simple agent scaffolding on 77 tasks across 30 task families testing autonomous capabilities.