Records found: 9
'A joint team from Anthropic and Thinking Machines Lab generated 300k+ value tradeoff scenarios to stress-test model specs, finding that high cross-model disagreement flags spec contradictions, coverage gaps and provider-level value differences.'
'Learn how to generate synthetic goldens with DeepEval to validate and benchmark your RAG pipeline before it goes live.'
'Google AI launched Stax, an experimental toolkit that helps developers evaluate and compare large language models using custom and pre-built autoraters and dataset-level analytics.'
'Australia relies on global LLMs while Kangaroo LLM aims to build a sovereign, Australian-tailored model, but remains in early data and governance phases as of August 2025.'
'AmbiGraph-Eval introduces a 560-example benchmark that measures LLM performance on ambiguous natural language to Cypher generation. Evaluations of nine models highlight challenges in ambiguity resolution and syntax generation.'
Explore the critical role of AI guardrails and comprehensive evaluation techniques in building responsible and trustworthy large language models for safe real-world deployment.
This tutorial shows how to use MLflow to evaluate Google Gemini's responses on factual prompts with integrated metrics, combining OpenAI and Google APIs for comprehensive LLM assessment.
AWS’s Automated Evaluation Framework offers a scalable, automated solution to improve the accuracy, fairness, and safety of Large Language Models, setting a new benchmark in AI reliability.
OpenAI has launched HealthBench, an open-source framework to rigorously evaluate large language models in healthcare using expert-validated multi-turn clinical conversations.