body of water between green leaf trees

How to make your bot like Raul

To improve the precision and adherence to specific details in prompts (often referred to as “prompt pedantry”), you can adjust various parameters in a language model’s input or configuration. Here are some effective strategies and parameters to focus on:

1. Explicit Instructions:

  • Clearly specify the details and constraints within the prompt itself. For example, if exact numerical details are crucial, explicitly state that they must be strictly followed.

2. Temperature:

  • Lower Temperature: Setting a lower temperature (e.g., 0.2 to 0.5) can make the model’s output more deterministic and focused, reducing the chances of deviation from specific details.
  • Example: temperature=0.3

3. Top-k Sampling:

  • Lower k Value: Using top-k sampling with a lower k value (e.g., 10 to 50) limits the model to choosing from the top k most probable next tokens, making it more likely to follow the prompt strictly.
  • Example: top_k=30

4. Top-p (Nucleus) Sampling:

  • Lower p Value: Setting a lower top-p value (e.g., 0.7 to 0.9) ensures the model considers only the most probable next tokens, increasing the likelihood of sticking closely to the prompt.
  • Example: top_p=0.85

5. Length Constraints:

  • Set Maximum and Minimum Lengths: Define strict maximum and minimum lengths for the output to ensure the response is concise and to the point.
  • Example: max_length=150, min_length=100

6. Repetition Penalty:

  • Adjust Repetition Penalty: Applying a repetition penalty discourages the model from repeating the same phrases, which can help maintain focus on delivering precise information.
  • Example: repetition_penalty=1.2

7. Custom Tokens or Anchors:

  • Use Custom Tokens: Embed specific keywords or phrases within the prompt to anchor the model’s response and ensure it covers required details.
  • Example: “Include exact numbers and maintain specific formatting as shown: {example format}.”

Example of a Well-Structured Prompt:

Generate a detailed description with precise numerical and spatial details. Ensure the output adheres to the following constraints:
- The description must include exactly 12 items.
- Arrange the items in a 4x3 grid.
- Use realistic details and maintain a white background.
- Follow the format and avoid any deviations.

Example: A realistic box containing 12 different speckled bird eggs arranged in a 4x3 grid on a white background. Each egg has unique speckle patterns and colors, showcasing a variety of bird species.

Combining Parameters in API Calls:

When making API calls, you can combine these parameters to fine-tune the model’s output. Here’s an example:

response = model.generate(
    prompt="A realistic box containing 12 different speckled bird eggs arranged in a 4x3 grid on a white background. Each egg has unique speckle patterns and colors, showcasing a variety of bird species.",
    temperature=0.3,
    top_k=30,
    top_p=0.85,
    max_length=150,
    min_length=100,
    repetition_penalty=1.2
)

Conclusion:

By carefully adjusting these parameters and crafting explicit prompts, you can significantly improve the model’s adherence to specific details, enhancing prompt pedantry.

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