Cognition · Data artifact
Few-Shot CoT Prompt
Data artifactCognitionCognition & Memoryarc:FewShotCoTPrompt
A chain-of-thought prompt embedding a few hand-crafted example problems with complete step-by-step solutions that demonstrate the expected reasoning pattern before the target question.
Responsibility. Demonstrates the required domain reasoning structure through worked exemplars.
Also known as: Few-shot Chain-of-Thought, Reasoning template
Variant of Chain-of-Thought Prompt abstract
When to choose. Choose when reasoning structure and consistency matter more than deployment speed (e.g., medical diagnosis, legal analysis, financial modelling) and expert time can be invested in two to three exemplary demonstrations per problem type.
Relationships
alternative to variability
Design guidance
- SHOULD prefer two to three high-quality, domain-matched demonstrations over many generic ones.
- SHOULD weigh measured accuracy gain against expert authoring effort, reserving hand-crafted demonstrations for high-stakes applications.
Quantitative guidance
As stated by the sources; verify before use.
- Up to 28.2% accuracy improvement on certain reasoning tasks versus zero-shot CoT (Ch5.1).
Classification
- Patterns
- Few-shot CoTIn-context learning
- Quality attributes
- Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
- Risks mitigated
- Vague, unstructured improvised reasoning
Sources
- Ch5.1: T. Nguyen, "Chain-of-Thought (CoT) Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.1. ISBN: 9798244538229.
- Ch10.1: T. Nguyen, "Conversational UI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.1. ISBN: 9798244538229.