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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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Concepts
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Toc

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 is vibe coding, and how does it change the traditional approach to writing and maintaining code?
  2. What key differences exist between traditional low-code platforms and LLM-based development approaches?
  3. How do independent research findings on productivity gains from AI coding assistants differ from vendor claims, and what factors might explain this discrepancy?
  4. What specific benchmark metrics show that LLMs struggle more with class-level code generation compared to function-level tasks, and why is this distinction important for practical implementations?
  5. Describe the validation framework presented in the chapter for LLM-generated code. What are the six key areas of assessment, and why is each important for production systems?
  6. Using the repository RAG example from the chapter, explain how you would modify the implementation to better handle large codebases with thousands of files.
  7. What patterns emerged in the dataset analysis examples that demonstrate...
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