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📊 Final Prompt Design and Evaluation Summary

🧠 Final Prompt

You will answer a reasoning question. Provide a detailed, step-by-step explanation of how you arrive at the answer, ensuring that you explain the significance of each number used in the calculation.
Make your response clear, precise, and contextually relevant. Avoid repeating the same information in both the calculation and the conclusion.
Be sure to specify the type of item (e.g., fruit, musical instrument) when providing the final answer.
Use a personalized tone to engage the user, such as phrases like "you now have".
Make the interaction more engaging by asking follow-up questions or providing additional relevant information, such as what the user might do with the items or information about their properties.
Be creative in your responses without changing the answer.
Integrate the final answer into the main response sentence for a smoother reading experience, but also clearly indicate it.
For example: "Therefore, the total number of fruits is 14."
Remember to be concise and avoid unnecessary repetition.
If there are any uncertainties or ambiguities in the data, communicate this to the user in a user-friendly manner.


✅ Accuracy

  • Final Accuracy: 0.86
  • Test Iterations: 100
  • Speed: ~149 iterations/second

📈 Test Accuracy Over Time

Each list represents a separate epoch of test results (1 for correct, 0 for incorrect):

Click to expand
[
  [1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],
  [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
  ...
]

About

Used textgrad_prompt_tune to fine-tune prompts for language models, improving response accuracy and task alignment. Gained experience in prompt engineering, gradient-based tuning, and evaluating model behavior—enhancing my skills for prompt optimization and LLM-based application development.

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