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Unlocking the Secrets of Prompt Engineering

You're reading from   Unlocking the Secrets of Prompt Engineering Master the art of creative language generation to accelerate your journey from novice to pro

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781835083833
Length 316 pages
Edition 1st Edition
Concepts
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Author (1):
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Gilbert Mizrahi Gilbert Mizrahi
Author Profile Icon Gilbert Mizrahi
Gilbert Mizrahi
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Introduction to Prompt Engineering FREE CHAPTER
2. Chapter 1: Understanding Prompting and Prompt Techniques 3. Chapter 2: Generating Text with AI for Content Creation 4. Part 2:Basic Prompt Engineering Techniques
5. Chapter 3: Creating and Promoting a Podcast Using ChatGPT and Other Practical Examples 6. Chapter 4: LLMs for Creative Writing 7. Chapter 5: Unlocking Insights from Unstructured Text – AI Techniques for Text Analysis 8. Part 3: Advanced Use Cases for Different Industries
9. Chapter 6: Applications of LLMs in Education and Law 10. Chapter 7: The Rise of AI Pair Programmers – Teaming Up with Intelligent Assistants for Better Code 11. Chapter 8: AI for Chatbots 12. Chapter 9: Building Smarter Systems – Advanced LLM Integrations 13. Part 4:Ethics, Limitations, and Future Developments
14. Chapter 10: Generative AI – Emerging Issues at the Intersection of Ethics and Innovation 15. Chapter 11: Conclusion 16. Index 17. Other Books You May Enjoy

Organizing unstructured data – using AI for automated text categorization and data classification

In the context of data classification, a trained LLM can analyze the various features in the dataset and classify data accurately. For instance, if the task is to categorize customer complaints into predefined classes, such as billing issues, technical issues, customer service issues, and so on, an LLM can analyze the text of the customer complaints, understand the context and the language used, and classify them into the respective categories accurately.

In many cases, the LLM should be able to make an accurate classification without the need for any training. However, in some cases, it will need to have some examples to understand the pattern that you want.

Typical uses of data classification include the following:

  • Distinguish between the different categories of data and assign each data point to its suitable category. Some examples are as follows:
    • Classifying incoming...
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