Intro to AI and Prompt Engineering Zero-Shot, One-Shot, and Few-Shot Prompting: The Importance of Consistent Prompt Training Few-Shot Prompting

Few-Shot Prompting

Few-shot prompting involves showing the AI two or more examples to help it better understand the task. This technique gives the model a broader context and a clearer idea of how to respond.

When to Use
Few-shot prompting is ideal when you want the AI to produce more accurate, detailed, or structured responses. By providing multiple examples, the AI can better grasp the pattern and expected format of the output.
Few-Shot Example
Prompt:
Add 2+2: 4
Add 5+5: 10
Add 6+6:
–
Here, we provide two examples (2+2 = 4 and 5+5 = 10) before asking the AI to solve a new problem (6+6). This method gives the AI a better understanding of the task, leading to a more reliable response.

Prompt:
Describe a player’s season stats: “Player: Mike Trout | Team: Los Angeles Angels | Games: 162 | Home Runs: 45 | Batting Average: .310 | RBIs: 104”
Describe a player’s season stats: “Player: Aaron Judge | Team: New York Yankees | Games: 158 | Home Runs: 52 | Batting Average: .285 | RBIs: 115”
Describe a player’s season stats:

————

Here, two examples (Mike Trout and Aaron Judge) are given to show the AI how to summarize baseball season stats for a player. The AI should use this structure to describe another player’s stats, following the format for details like games played, home runs, batting average, and RBIs.

Why Few-Shot Prompting is Preferred

Few-shot prompting often works better than zero-shot or one-shot prompting because showing multiple examples provides the AI with more context and guidance. This approach allows the model to more accurately understand the task and produce the correct format or output.

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