According to @GoogleDeepMind, the new FACTS Benchmark Suite, developed in collaboration with @GoogleResearch, is the industry's first comprehensive evaluation tool specifically designed to measure the factual accuracy of large language models (LLMs) across four key dimensions: internal model knowledge, web search capabilities, grounding, and multimodal inputs (source: Google DeepMind on Twitter). This benchmark enables AI developers and businesses to reliably assess and improve LLM factuality, driving advancements in trustworthy AI applications and enhancing commercial opportunities in sectors demanding high factual precision.

According to @godofprompt on Twitter, implementing structured prompt engineering techniques—such as guiding AI models through planning, execution, and verification steps—dramatically improves output accuracy. Instead of generic prompts like 'do the thing,' providing a scaffolded approach enables AI models to deliver more reliable results. The difference between 70% and 95% accuracy is often attributed to prompt design rather than the underlying model's capabilities (source: @godofprompt, Dec 10, 2025). This insight highlights a major business opportunity: by investing in advanced prompt engineering, enterprises can unlock greater value from existing AI systems without costly model upgrades, directly impacting operational efficiency and competitive advantage.