Lecture
Fine-Tuned Model Specializing in Tabular Data
To effectively understand data at a glance, organizing it in a tabular format is often most effective.
In this lesson, we will compare a general AI model with a fine-tuned model specializing in tabular data and observe the differences in their response results.
Differences from Prompt Engineering
When organizing data in tabular form is necessary, Prompt Engineering involves explicitly including requests like Please organize the data in a table format in the prompt.
In contrast, a fine-tuned model is retrained to organize responses to various questions in a tabular format, providing organized responses without the need for explicit requests within the prompt.
Practice
- Copy the Prompt: Click the
Copybutton at the top right of the box below to paste it into the input box at the bottom right.
Prompt Example
Please create a weekly workout schedule from Monday to Sunday. The workout will last one hour each day. If a rest day is needed, please include it.
- Execute Report Writing: Send the selected prompt to have AI generate a report. Compare the output results on both screens.
Lessons in this chapter · Mastering the Basics
- 1. Transforming AI for Custom Tasks (Fine-Tuning)
- 2. How Do They Differ? (Fine-Tuning vs Prompt Engineering)
- 3. What are the Benefits of Fine-tuning?
- 4. Experience a Fine-Tuned Model with MBTI
- 5. Fine-Tuned Model Specializing in Tabular Data
- 6. Multiple-choice quiz
- 7. A Beginners Guide to AI (Machine Learning Terminology)
- 8. What is Deep Learning?
- 9. How Do Machine Learning and Deep Learning Differ?
- 10. What Does It Mean for AI to Learn?
- 11. How Does Fine-Tuning Work?
- 12. Fill-in-the-blank quiz
- 13. Fine-Tuning Practice
Quiz
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Please create a fine-tuned model.