Prompt Performance Optimization - Hands On

Now that we are hands-on with prompt engineering, let’s practice using different configurations to see how we can influence the creativity of a model using Claude 3 Sonnet on AWS.

Initial Setup

• We select Claude 3 Sonnet from Anthropic as our model.
• The prompt we enter: "Please write a short story about a robot learning how to cook."
• We define the story to be short.
• The maximum length is set to 600 tokens to ensure brevity.

Running with Conservative Settings

We begin with low creativity settings by configuring:

• Temperature: Low
• Top P: Low
• Top K: Low

These settings are known to generate more conservative and predictable outputs.

Result:

• The model outputs a story with a kitchen scene, a chef, and a robot.
• While the output looks interesting at a glance, it reads as plain and potentially boring.

Increasing Creativity

Now we modify the settings to boost the model’s creativity:

• Temperature: Increased
• Top P: Set to maximum
• Top K: Set to 500

These changes allow the model to explore a wider range of vocabulary and creative paths.

New Prompt (same as before):

• "Please write a short story about a robot learning how to cook."

Result:

• The output becomes much more creative.

• Elements include:

  • Optical sensors
  • A human instructor
  • Cooking crepes
  • The robot even tries eating the food

Comparison and Summary

• Both low-temperature and high-temperature prompt outputs will be saved in the code directory for comparison.
• This exercise shows how different configurations affect the output.

Key Takeaways

• Temperature: Controls the overall creativity of the model.
• Top P: Determines the percentile of word probabilities considered.
• Top K: Specifies how many words are considered for the next word prediction.

Hopefully, this demonstration helped you understand how model configurations influence outputs.