Chain of thought reasoning is a method in 📝Artificial Intelligence (AI) and cognitive science that involves articulating intermediate steps between a problem and its solution. Chain of thought reasoning has been studied extensively in the context of 📝Large Language Model (LLM)s, where it enables systems to improve performance on tasks requiring logic, arithmetic, or multi-step problem-solving. Researchers such as Jason Wei and colleagues introduced the term in 2022, demonstrating that prompting models with explicit step-by-step reasoning improves accuracy on complex benchmarks. This approach aligns with earlier traditions in cognitive psychology, where reasoning was examined as a sequence of inferential steps rather than as a single outcome. In modern AI systems, chain of thought is often implemented through 📝Prompt Engineering or fine-tuning methods, allowing the model to generate interpretable reasoning paths. Its applications span mathematics, commonsense reasoning, and planning tasks, though ongoing debates focus on issues of reliability, 📝hallucination, and whether models are genuinely reasoning or simply pattern-matching. The method continues to be a significant subject of both academic inquiry and applied 📝Machine Learning (ML) practice.
