Usage
Chain-of-Thought (CoT)
A prompting strategy where the model is encouraged to produce intermediate reasoning steps before arriving at a final answer, improving performance on complex tasks.
Explained at five levels
Level 1
When the AI shows its work step by step, like how your teacher wants you to show how you solved the math problem.
Level 2
A prompting technique where you ask the AI to think through a problem step by step instead of jumping to the answer — it usually gives better results.
Level 3
A prompting strategy where the model is encouraged to produce intermediate reasoning steps before arriving at a final answer, improving performance on complex tasks.
Level 4
An elicitation technique that prompts the model to decompose complex reasoning into explicit intermediate steps, improving accuracy on arithmetic, logic, and multi-hop questions through serial computation.
Level 5
A test-time compute scaling strategy that extends the model's effective reasoning depth by eliciting explicit intermediate tokens — trading increased generation length for improved accuracy on compositional tasks, analyzable through the lens of computational complexity and scratchpad augmentation.
Definitions are educational summaries. Terminology can vary by source and context.
Sources
- NIST Trustworthy and Responsible AI Resource Center: Glossary — National Institute of Standards and Technology. Accessed 2026-07-20.