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Generative AI with LangChain

You're reading from   Generative AI with LangChain Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph

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Product type Paperback
Published in May 2025
Publisher Packt
ISBN-13 9781837022014
Length 476 pages
Edition 2nd Edition
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Table of Contents (14) Chapters Close

Preface 1. The Rise of Generative AI: From Language Models to Agents 2. First Steps with LangChain FREE CHAPTER 3. Building Workflows with LangGraph 4. Building Intelligent RAG Systems 5. Building Intelligent Agents 6. Advanced Applications and Multi-Agent Systems 7. Software Development and Data Analysis Agents 8. Evaluation and Testing 9. Production-Ready LLM Deployment and Observability 10. The Future of Generative Models: Beyond Scaling 11. Other Books You May Enjoy 12. Index Appendix

Questions

  1. What are the key components of a pre-deployment checklist for LLM agents and why are they important?
  2. What are the main security risks for LLM applications and how can they be mitigated?
  3. How can prompt injection attacks compromise LLM applications, and what strategies can be implemented to mitigate this risk?
  4. In your opinion, what is the best term for describing the operationalization of language models, LLM apps, or apps that rely on generative models in general?
  5. What are the main requirements for running LLM applications in production and what trade-offs must be considered?
  6. Compare and contrast FastAPI and Ray Serve as deployment options for LLM applications. What are the strengths of each?
  7. What key metrics should be included in a comprehensive monitoring strategy for LLM applications?
  8. How do tracking, tracing, and monitoring differ in the context of LLM observability, and why are they all important?
  9. What are the different patterns...
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