Prompting
Chain of Thought (CoT)
A prompting technique where the model is asked to show its step-by-step reasoning before arriving at a final answer.
In depth
A prompting technique where the model is asked to show its step-by-step reasoning before arriving at a final answer. CoT improves accuracy on math, logic, and coding tasks by forcing the model to decompose problems rather than jumping to conclusions. Reasoning models like o1 and o3 use chain-of-thought internally as part of their training.
Example
Reasoning models like o1 and o3 use chain-of-thought internally as part of their training.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Prompting.
FAQ
What is Chain of Thought (CoT)?
A prompting technique where the model is asked to show its step-by-step reasoning before arriving at a final answer.
Why does Chain of Thought (CoT) matter for AI developers?
Chain of Thought (CoT) sits in the Prompting part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Chain of Thought (CoT)?
Developers Digest publishes tutorials and videos that cover Prompting topics including Chain of Thought (CoT). Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
Prompting
Few-Shot Learning
A prompting technique where you include a small number of input-output examples in the prompt to show the model the pattern you want it to follow.
Prompting
Zero-Shot Learning
A model's ability to perform a task it was not explicitly trained on, using only the instructions in the prompt with no examples.
Prompting
Guardrails
Safety constraints and validation layers applied to AI model inputs and outputs.
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