Critical thinking in AI refers to a model's ability to analyze information deeply, understand nuanced contexts, and draw logical connections to reach coherent conclusions, mimicking human reasoning processes. Evaluating critical thinking skills is essential for ensuring models can handle complex tasks, reason logically, and provide reliable outputs beyond simple text generation. With the growing emphasis on AI regulation, assessing these skills helps identify areas needing improvement, ensuring AI systems are reliable, effective, and ready for real-world applications. Various benchmarks focus on different aspects of reasoning and problem-solving, such as logical reasoning tests and problem-solving benchmarks that examine how well a model interprets questions and devises logical solutions. Tools like Galileo evaluate logical reasoning in LLMs by using techniques like Reflexion and external reasoning modules, providing insights into how models approach reasoning tasks. Platforms like Galileo are designed to test and enhance problem-solving capabilities, focusing on model performance and AI compliance. Ethical decision-making is a critical aspect of deploying AI responsibly, and tools like TruthfulQA have become increasingly critical in ensuring models provide accurate and trustworthy information. By passing benchmarks, models demonstrate their ability to provide accurate and trustworthy information, which is essential for maintaining organizational integrity and public confidence. To effectively evaluate LLMs for critical thinking abilities, several criteria include context adherence, PII, and custom metrics, and best practices include using specific criteria in benchmarking processes, employing a variety of metrics, and allowing for custom metrics to tailor evaluations to specific project needs. Platforms like Galileo offer engineers reliable and actionable insights through continuous monitoring and evaluation intelligence capabilities, facilitating swift identification and resolution of issues, enhancing the reliability of insights provided to engineers. Analyzing results helps pinpoint where models may be falling short, and detailed error analysis allows for identifying areas for model improvement. Practitioners use a combination of benchmarks to evaluate models, and tools like Galileo support standard benchmarks and allow integration of custom datasets for a tailored evaluation experience. To enhance critical thinking skills in LLMs, targeted strategies focusing on specific reasoning abilities can be employed, and platforms like Galileo provide tools for fine-tuning with domain-specific datasets and optimizing prompts and model settings. As language models advance, evaluating their critical thinking abilities is changing, and researchers are introducing new methods to better assess complex reasoning. Platforms like Galileo offer capabilities that align with AI advancements, providing expertise and tools for various AI projects, including chatbots, internal tools, and advanced workflows. By utilizing tools such as Galileo, engineers can enhance the effectiveness and relevance of their models.