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Building AI Applications with Microsoft Semantic Kernel

You're reading from   Building AI Applications with Microsoft Semantic Kernel Easily integrate generative AI capabilities and copilot experiences into your applications

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781835463703
Length 252 pages
Edition 1st Edition
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Author (1):
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Lucas A. Meyer Lucas A. Meyer
Author Profile Icon Lucas A. Meyer
Lucas A. Meyer
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Toc

Table of Contents (14) Chapters Close

Preface 1. Part 1:Introduction to Generative AI and Microsoft Semantic Kernel
2. Chapter 1: Introducing Microsoft Semantic Kernel FREE CHAPTER 3. Chapter 2: Creating Better Prompts 4. Part 2: Creating AI Applications with Semantic Kernel
5. Chapter 3: Extending Semantic Kernel 6. Chapter 4: Performing Complex Actions by Chaining Functions 7. Chapter 5: Programming with Planners 8. Chapter 6: Adding Memories to Your AI Application 9. Part 3: Real-World Use Cases
10. Chapter 7: Real-World Use Case – Retrieval-Augmented Generation 11. Chapter 8: Real-World Use Case – Making Your Application Available on ChatGPT 12. Index 13. Other Books You May Enjoy

Retrieval-augmented generation

RAG is an approach that combines the powers of pre-trained language models with information retrieval to generate responses based on a large corpus of documents. This is particularly useful for generating informed responses that rely on external knowledge not contained within the model’s training dataset.

RAG involves three steps:

  • Retrieval: Given an input query (for example, a question or a prompt), you use a system to retrieve relevant documents or passages from your data sources. This is typically done using embeddings.
  • Augmentation: The retrieved documents are then used to augment the input prompt. Usually, this means creating a prompt that incorporates the data from the retrieval step and adds some prompt engineering.
  • Generation: The augmented prompt is then fed into a generative model, usually GPT, which generates the output. Because the prompt contains relevant information from the retrieved documents, the model can generate...
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