For complex tasks, the model often benefits from being asked to reason explicitly. Chain-of-thought (CoT) prompting instructs the model to reason step-by-step before giving a final answer, which improves accuracy on tasks requiring multi-step logic. The trigger phrase "Let's think step by step" or "Think through this carefully" encourages this structured reasoning, and zero-shot chain-of-thought combines this phrase with no examples to obtain reasoning purely from the model's own knowledge. Building on this, self-consistency prompting generates multiple reasoning paths for the same problem and selects the most common answer, while tree-of-thought (ToT) prompting has the model explore multiple reasoning branches simultaneously and pick the most promising path. ReAct prompting interleaves Reasoning and Acting, allowing the model to call tools as part of an extended thought process.
Decomposition is another powerful idea. Task decomposition in prompting breaks a large task into smaller sub-tasks that are prompted and solved individually, and prompt chaining sequences these so that the output of one becomes the input of the next. Least-to-most prompting decomposes a problem into sub-problems and solves them in order from simplest to most complex, while self-ask prompting has the model generate and answer its own follow-up questions. The "step back" technique asks the model to first identify an abstract principle behind a question before answering the specific case, and skeleton-of-thought prompting has the model produce an outline first and then flesh out each part, enabling parallelism and speed. Maieutic and Socratic prompting ask the model to explain its reasoning and then probe for inconsistencies, much like the Socratic method, and the "ask for alternatives" technique prompts the model to generate multiple solutions to increase diversity.
Several specialized techniques extend this reasoning toolkit. The devil's advocate technique surfaces counterarguments by asking the model to argue against a position, while the rubber duck technique asks the model to explain a concept in simple terms to verify understanding. The Columbo technique goes further by asking one final seemingly simple follow-up question that prompts the model to reveal deeper reasoning. Chain-of-verification (CoVe) has the model generate an answer, create verification questions about it, and answer each one independently, and the critique and revise loop produces an answer, critiques it, and then produces an improved version. Analogical prompting asks the model to draw on related domains to solve a new problem, and the thought experiment prompt invites reasoning about hypothetical scenarios to stress-test logic before applying it to real cases.