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Glossary · Tooling

Chain-of-Thought (CoT)

Also known as: CoT, Chain of thought, Step-by-step reasoning

Ask the model to think step by step before answering. It boosts accuracy on reasoning tasks.

A prompting technique that asks the model to produce intermediate reasoning steps before the final answer. Improves performance on multi-step reasoning tasks; closely related to how 'reasoning models' like o1 and DeepSeek-R1 are trained.

In practice

Foundational prompting pattern. Modern reasoning models bake CoT into their RL training, so understanding it explains o1/R1's behavior.

How it compares

vs. Prompt Engineering →

CoT is a specific prompting technique; prompt engineering is the broader practice.

Comparisons that include Chain-of-Thought (CoT)

Chain-of-thought vs Tree-of-thoughtCoT reasons in a line; ToT explores multiple branches, evaluates them, and keeps the promising ones. ToT costs many more tokens but wins on hard search problems like puzzles.Zero-shot vs Few-shot vs Chain-of-thoughtZero-shot asks the model to do a task cold; few-shot shows examples; chain-of-thought asks the model to reason step by step. They compose: few-shot chain-of-thought is often stronger than either alone.

Related topics

AI Agents

110 questions

Related terms

Prompt Engineering

Crafting the input text so the model produces the behavior you want.

AI Agents

LLMs that loop: plan → call tools → observe results → repeat until done.

Model Context Protocol (MCP)

An open protocol (from Anthropic) for letting LLMs call tools and read resources from any compatible server.

In-Context Learning (ICL)

Show the model a few examples in the prompt and it learns the pattern, no fine-tuning needed.

Function Calling

The model emits structured JSON specifying a tool call instead of free text; the host executes it.

Prompt Injection

Attacker text in user input or retrieved content overrides the model's system prompt or hijacks tool calls.