Chain-of-Thought Prompting: Make LLMs Reason Step by Step

A tutorial on chain-of-thought prompting explains how to make large language models reason step by step, covering techniques like query rewriting, hypothetical document embeddings, and self-reflective retrieval. The guide also includes methods for evaluating agent performance, preventing harmful actions, and building agents that interact with APIs and databases.

Sorry, we couldn't find this page. But don't worry, you can explore our tutorials or return to the homepage. Apply query rewriting, hypothetical document embeddings, and self-reflective retrieval. Learn how to measure whether your agent is actually working with rigorous evaluation. Prevent agents from taking harmful actions using human-in-the-loop and constraint patterns. Build a complete agent that searches, synthesizes, and produces structured reports. Build agents that interact with REST APIs, databases, and file systems. Break down every effective prompt into its four core components and learn when to use each one.