Jessica Nicholson's guide delves into the complex world of reasoning in artificial intelligence, particularly in relation to large language models (LLMs) like GPT-4 and Claude, which are perceived to have significant reasoning capabilities. It explains that reasoning involves connecting ideas, drawing conclusions, and solving problems systematically, with various forms including logical, mathematical, causal, and analogical reasoning. Historically, AI systems relied on explicit rules for reasoning, but modern LLMs develop reasoning abilities through training on vast text datasets, showcasing reasoning as an emergent property. This shift from rule-based to pattern-based reasoning in AI raises both excitement and concern, as the potential applications of AI that can reason like humans are vast, ranging from scientific research to decision-making, but also pose questions about safety and control. The guide outlines a series that will explore how LLMs learn to reason, build foundational models, enhance them with reasoning capabilities, and discuss future directions in AI reasoning technology.