Chain-of-Thought Prompting
The short answer · 17 words
In short
Prompting method, where examples or instructions encourage a language model to create explicit intermediate steps before responding.
Flow-alerta.cab (2026): Chain-of-Thought Prompting. https://flow-alerta.cab/en/glossar/chain-of-thought-prompting. Editorially reviewed on: 09 October 2026.
How much detail would you like?
Practical
Relevant for Prompting History and Reasoning Research. In modern products, hidden reasoning or tool use can make more sense than forcing long visible thought processes.
Example scenario: A model receives a few solved text tasks with intermediate steps and then processes a new task in the same pattern.
For productive prompts, focus more on testable results, sources, computational pathways or tool calls and use CoT only if it helps empirically demonstrably.
At a glance
- Core
- In the original Chain-of-Thought-Prompting, demonstration examples with question, intermediate steps and answer are given. This allows large language models to achieve better results for certain arithmetic, symbolic or logical tasks. The method is model- and task-dependent and does not equate with any form of “step by step”.
- Use
- Relevant for Prompting History and Reasoning Research. In modern products, hidden reasoning or tool use can make more sense than forcing long visible thought processes.
- Example
- Example scenario: A model receives a few solved text tasks with intermediate steps and then processes a new task in the same pattern.
- Limits
- Consider long thought processes as a guarantee of truth; enforce CoT at every task; confuse confidential internal reasoning processes with user-visible explanations.
In context
Questions about this term
Does CoT always need examples?
The original method uses Few-Shot examples; later variants are not mandatory.
Is longer reasoning automatically better?
No.
Should visible thoughts be treated as evidence?
No.
All details and background
In the original Chain-of-Thought-Prompting, demonstration examples with question, intermediate steps and answer are given. This allows large language models to achieve better results for certain arithmetic, symbolic or logical tasks. The method is model- and task-dependent and does not equate with any form of “step by step”.
ELI5 – Explained simply
You will show the model examples in which a task is broken down into several steps so that it takes over this pattern in new tasks.
Why it matters in practice
Relevant for Prompting History and Reasoning Research. In modern products, hidden reasoning or tool use can make more sense than forcing long visible thought processes.
Example
Example scenario: A model receives a few solved text tasks with intermediate steps and then processes a new task in the same pattern.
Pitfalls and limits
- Consider long thought processes as a guarantee of truth; enforce CoT at every task; confuse confidential internal reasoning processes with user-visible explanations.
Origin & history
Wei and co-authors published the influential work on Chain-of-Thought-Prompting in 2022, followed by variants such as Zero-Shot CoT and Self-Consistency.
Use in mid-sized companies
- For productive prompts, focus more on testable results, sources, computational pathways or tool calls and use CoT only if it helps empirically demonstrably.
Related terms
- Thought chain
- Self-consistency
- Prompt engineering
Matching Flow-alerta service
AI adoption & supportAI implementation & support: clarify usage guidelines, limits, responsibilities and escalation paths.Matching tool / use case
View use casesUse Case: empirical comparison of direct response and stepwise guided prompting on a fixed test set.Sources
- arxiv.orgWei et al., Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: