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Building LLM Powered  Applications

You're reading from   Building LLM Powered Applications Create intelligent apps and agents with large language models

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835462317
Length 342 pages
Edition 1st Edition
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Author (1):
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Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Large Language Models 2. LLMs for AI-Powered Applications FREE CHAPTER 3. Choosing an LLM for Your Application 4. Prompt Engineering 5. Embedding LLMs within Your Applications 6. Building Conversational Applications 7. Search and Recommendation Engines with LLMs 8. Using LLMs with Structured Data 9. Working with Code 10. Building Multimodal Applications with LLMs 11. Fine-Tuning Large Language Models 12. Responsible AI 13. Emerging Trends and Innovations 14. Other Books You May Enjoy
15. Index

What are structured data?

In previous chapters, we focused on how Large Language Models can handle textual data. In fact, those models are, as the name suggests, “language” models, meaning that they have been trained and are able to handle unstructured, text data.Nevertheless, unstructured data only refers to a portion of the overall data realm that applications can handle. Generally, data can be categorized into three types:Certainly! Here are definitions and examples of unstructured, structured, and semi-structured data:

  • Unstructured Data refers to data that doesn't have a specific or predefined format. It lacks a consistent structure, making it challenging to organize and analyze using traditional databases. Examples of unstructured data include:
    • Text documents: Emails, social media posts, articles, and reports.
    • Multimedia: Images, videos, audio recordings.
    • Natural language text: Chat logs, transcriptions of spoken conversations.
    • Binary data: Files without a specific...
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