Learning Paradigms in Prompt Engineering
Zero-Shot Learning：
Directly asking the model to perform a task it has never been explicitly trained on, without any additional examples.
This means that the LLM relies solely on the model’s pre-trained knowledge, with no additional context or demonstration provided.
Results in lower accuracy response for complex or domain-specific tasks.
One-Shot Learning：
Providing a single example in the prompt to help the model understand the expected output.
This will offer a clearer context compared to zero-shot learning, helping the LLM model understand the task’s pattern and desired format.
Typically used in structured output tasks that require style or formatting.
Few-Shot Learning：
Including 2-5 examples in the prompt to enhance the model’s task understanding.
By providing richer context, you can help LLM learn subtle nuances of the task. Significantly improves model performance on specific tasks.
Allows fine-tuning of domain-specific understanding.
Make the AI agent cover different variations and edge cases of the task.
Hope the above sharing is helpful to you.
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