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RAG-Driven Generative AI

You're reading from   RAG-Driven Generative AI Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836200918
Length 334 pages
Edition 1st Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Toc

Table of Contents (14) Chapters Close

Preface 1. Why Retrieval Augmented Generation? FREE CHAPTER 2. RAG Embedding Vector Stores with Deep Lake and OpenAI 3. Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI 4. Multimodal Modular RAG for Drone Technology 5. Boosting RAG Performance with Expert Human Feedback 6. Scaling RAG Bank Customer Data with Pinecone 7. Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex 8. Dynamic RAG with Chroma and Hugging Face Llama 9. Empowering AI Models: Fine-Tuning RAG Data and Human Feedback 10. RAG for Video Stock Production with Pinecone and OpenAI 11. Other Books You May Enjoy
12. Index
Appendix

Questions

Answer the following questions with Yes or No:

  1. Is human feedback essential in improving RAG-driven generative AI systems?
  2. Can the core data in a generative AI model be changed without retraining the model?
  3. Does Adaptive RAG involve real-time human feedback loops to improve retrieval?
  4. Is the primary focus of Adaptive RAG to replace all human input with automated responses?
  5. Can human feedback in Adaptive RAG trigger changes in the retrieved documents?
  6. Does Company C use Adaptive RAG solely for customer support issues?
  7. Is human feedback used only when the AI responses have high user ratings?
  8. Does the program in this chapter provide only text-based retrieval outputs?
  9. Is the Hybrid Adaptive RAG system static, meaning it cannot adjust based on feedback?
  10. Are user rankings completely ignored in determining the relevance of AI responses?
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