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AI Models & Assistants / Knowledge library
Chain-of-Thought (CoT) Prompting
Chain of thought prompts with intermediate reasoning steps
Why learn Chain-of-Thought (CoT) Prompting?
Useful for investigating multi-step task performance. Score final correctness and verify claims independently; a readable explanation is not proof of faithful reasoning.
The problem it solves
Provides a prompting approach for decomposing tasks into intermediate steps that can improve performance on suitable problems.
Where it came from
Chain-of-thought prompting encourages intermediate reasoning steps before an answer, often through worked examples. Research demonstrated gains on some arithmetic, symbolic, and commonsense tasks. Follow-up studies showed that a plausible explanation can omit influences that actually changed a model's answer.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Create worked examples for a small arithmetic evaluation set.
- Compare answer-only and worked-example prompts using the same questions.
- Check every numerical step against a calculator.
- Reorder multiple-choice options and inspect changes in accuracy.
- Find a plausible explanation with an incorrect answer and identify the error.
Documentation and sources
- Original chain-of-thought prompting paper (arxiv.org)
- Research on unfaithful CoT explanations (arxiv.org)
Connect the ideas
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Real World Example
Automated Logistics Route Optimization
A regional logistics firm managing complex multi-stop delivery schedules for perishable goods.
The business problem
Standard prompt engineering often leads to incorrect delivery sequencing because the model fails to account for intermediate constraints like vehicle capacity, driver shift limits, and traffic windows simultaneously.
How this topic solves it
Chain-of-Thought (CoT) prompting forces the model to decompose the route planning task into a series of intermediate reasoning steps, such as calculating load weight per stop and checking time-window feasibility before finalizing the sequence.
- Define a set of few-shot examples that include a complex delivery scenario and a step-by-step breakdown of the logic.
- Structure the prompt to explicitly request an intermediate reasoning trace before the final route output.
- Integrate constraints as individual logical steps within the reasoning chain.
- Validate the output by ensuring the reasoning trace aligns with the final delivery sequence.
This diagram illustrates how the model processes a request by moving from raw input to a structured reasoning chain before producing the final route.
Intermediate logical steps
Validation of capacity/time
Optimized delivery sequence
- Input Request → triggers → Reasoning Trace
- Reasoning Trace → validates → Constraint Check
- Constraint Check → produces → Final Route
Expected business outcome
Improved accuracy in complex route planning by ensuring the model explicitly considers all operational constraints during the generation process.
Limitations and checks
Increased token consumption and latency due to the generation of intermediate reasoning text; monitor this by tracking the average completion time per request.