Chain-of-Thought
Chain-of-thought prompting transforms AI from answer machine to reasoning partner by explicitly modeling the problem-solving process within the prompt itself. Born from Google Research's 2022 breakthrough, this technique demonstrates that showing your work isn't just good practice—it fundamentally improves AI performance.

Rather than jumping to conclusions, chain-of-thought breaks complex problems into logical steps, creating a cognitive roadmap the AI can follow and extend. It's the difference between asking for directions and teaching someone to read a map.
Using chain-of-thought:
- Decompose complex queries into sequential reasoning steps
- Include intermediate calculations and logical transitions
- Make implicit thinking explicit through worked examples
The technique's elegance lies in its universality—any process that can be articulated can be enhanced. Organizations that master chain-of-thought prompting aren't just getting better outputs; they're documenting and scaling their collective intelligence.
Related terms:
Structured Output
Structured output occurs when a language model returns data in predictable, machine-readable formats—such as JSON, XML, or typed objects—rather than...
AI Agent
An AI agent is a system that autonomously breaks a goal into steps—calling tools, reading results, and adjusting course—without waiting for a human prompt.
Agentic Workflows
Agentic workflows are multi-step AI processes where the system autonomously plans, executes, and iterates tasks—researching, drafting, reviewing, and...