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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