Few-shot Learning
A technique that includes 2 to 10 input-output examples in the prompt without retraining the model, enabling the AI to immediately follow the patterns and formats of the task.
Detailed explanation
Why it matters in tool selection
Few-shot learning enables performance similar to a 'specialized model' without large-scale data labeling and expensive fine-tuning. It is especially efficient when implementing chatbots that need to maintain a specific persona or process unique internal corporate document formats, as it refines execution instructions while utilizing the model's base intelligence.
What to check
- Is the format of the examples (labeling, line breaks, etc.) maintained consistently across all shots?
- Are the situations covered by the examples non-overlapping and inclusive of diverse cases?
- Does the number of shots not excessively occupy the model's context window (token limit)?
- Does the order of examples not create bias? (Check tendency to recognize recent examples as more important)
Example: Sentiment Analysis
Input: 'I'm annoyed because the delivery is so late.' // Output: Negative Input: 'The counselor is kind, so I feel good.' // Output: Positive Input: 'The product is average, but the packaging is poor.' // Output: ?
Confusing terms
Zero-shot
A method of generating responses using only instructions (System Prompt) without any examples.
Fine-tuning
A method of permanently training the model by directly modifying its weights (parameters) using thousands or more data entries.