Types of Prompts — Zero-shot, Few-shot, and Chain-of-Thought
Different prompting styles significantly influence how AI language models interpret tasks and generate responses. Zero-shot prompting relies on the model's internal knowledge without examples, while few-shot prompting enhances understanding through examples. Chain-of-thought prompting encourages step-by-step reasoning, improving outcomes particularly for complex tasks. Selecting the appropriate prompting style based on the task can dramatically enhance effectiveness.
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What we have learnt
- Zero-shot prompting is best for simple, fact-based tasks.
- Few-shot prompting is effective for tasks requiring tone, style control, or specific formatting.
- Chain-of-thought prompting improves accuracy in reasoning and reduces complex problem hallucinations.
Key Concepts
- -- Zeroshot prompting
- A prompting style where the model is given a task without any examples, relying solely on its pre-existing knowledge.
- -- Fewshot prompting
- A prompting style that provides the model with a few examples to clarify task format or desired output.
- -- Chainofthought prompting
- A prompting style that instructs the model to think through the problem step-by-step before arriving at a solution.
- -- Prompt engineering
- The practice of designing effective prompts to achieve desired outcomes in AI model responses.
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