Reasoning Elicitation
Chain-of-thought, tree-of-thought, and advanced reasoning techniques.
Chain-of-Thought Prompting
Chain-of-thought prompting dramatically improves LLM reasoning by including step-by-step worked examples that teach the model to show its work before answering. Prerequisites: 02-core-prompting-techniques/few-shot-prompting.md, 01-foundations/how-llms-process-prompts.md
Extended Thinking and Thinking Budgets
Extended thinking gives LLMs a dedicated, often hidden, token budget for internal reasoning before producing a visible response, formalizing the insight that harder problems benefit from more “thinking time.” Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 01-foundations/how-llms-process-prompts.md, 01-foundations/context-window-mechanics.md
Metacognitive Prompting
Metacognitive prompting asks the model to reflect on its own knowledge, confidence, and reasoning quality, producing better-calibrated outputs that distinguish what the model knows from what it does not. Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 01-foundations/how-llms-process-prompts.md
Self-Ask and Decomposition
Self-ask prompting teaches the model to break complex questions into smaller sub-questions, answer each independently, and synthesize the results into a final answer. Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 02-core-prompting-techniques/few-shot-prompting.md
Self-Consistency
Self-consistency improves chain-of-thought reasoning by sampling multiple reasoning paths at non-zero temperature and selecting the most common final answer through majority voting. Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 01-foundations/temperature-and-sampling.md
Step-Back Prompting
Step-back prompting improves reasoning by first asking the model to identify the relevant high-level principle or concept before attempting to solve the specific problem. Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 02-core-prompting-techniques/few-shot-prompting.md
Structured Reasoning Formats
Structured reasoning formats provide explicit templates – such as OTA, Given-Find-Solution, and Claim-Evidence-Reasoning – that guide the model’s reasoning into a predictable, task-appropriate structure. Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 02-core-prompting-techniques/few-shot-prompting.md
Tree-of-Thought Prompting
Tree-of-thought prompting extends chain-of-thought from a single linear reasoning path to a branching search tree, enabling the model to explore, evaluate, and backtrack through multiple reasoning strategies. Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 03-reasoning-elicitation/self-consistency.md
Zero-Shot Chain-of-Thought
Adding “Let’s think step by step” to a prompt – with no examples at all – can dramatically improve reasoning performance by triggering the model’s latent step-by-step generation capabilities. Prerequisites: 03-reasoning-elicitation/chain-of-thought-prompting.md, 01-foundations/how-llms-process-prompts.md