reasoning
6 results
Self-Consistency: Get Better Answers by Sampling Multiple Reasoning Paths
Self-consistency generates multiple chain-of-thought responses and takes the majority vote. Learn how it dramatically improves accuracy on reasoning tasks and when to use it.
ReAct Prompting: Reasoning + Acting in a Loop
ReAct interleaves reasoning (Thought) and action (Act) steps so an AI agent can plan, use tools, and adjust its approach based on real-world feedback — all within a single prompt loop.
ReAct Prompting: Reason Before You Act
ReAct is the reasoning pattern that makes agents dramatically more reliable. By explicitly writing out thoughts before every action, the model plans better, catches errors earlier, and produces work you can follow and debug.
Chain of Thought Prompting: The Complete Guide
Learn how Chain of Thought (CoT) prompting forces AI models to reason step-by-step, dramatically improving results for math, logic, and complex reasoning tasks.
Chain of Thought Prompting: Make AI Reason Step by Step
Chain of Thought (CoT) prompting forces AI to show its reasoning before answering — dramatically improving accuracy on logic, math, analysis, and multi-step tasks.
Tree of Thought: Multi-Path Reasoning for Complex Problems
Tree of Thought prompting extends Chain of Thought by exploring multiple reasoning paths simultaneously — dramatically improving performance on complex planning, creative, and decision-making tasks.