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Learn Mistral

You're reading from   Learn Mistral Elevating Mistral systems through embeddings, agents, RAG, AWS Bedrock, and Vertex AI

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Product type Paperback
Published in Oct 2025
Publisher Packt
ISBN-13 9781835888643
Length 528 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Pavlo Cherkashin Pavlo Cherkashin
Author Profile Icon Pavlo Cherkashin
Pavlo Cherkashin
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Table of Contents (14) Chapters Close

Preface 1. Strengths, Limitations, and Use Cases of Language Models FREE CHAPTER 2. Setting Up Your Own Chat 3. Managing the Model 4. Mastering Embeddings 5. Agents: From Automation to Intelligence 6. Unpacking RAG Workflows 7. Coding with Mistral 8. Building Smarter Defenses with Mistral 9. Take-Home RAG Challenges 10. Mistral on AWS Bedrock 11. Harnessing Mistral’s Power via Google Cloud Vertex AI 12. Other Books You May Enjoy
13. Index

Workshop 4: Generification approach

In Workshop 4, we take a new approach to refining our retrieval and generation process by creating step-back questions. These are broader, more generalized versions of the original user question. Instead of narrowing down the focus, as we did with drill-down sub-questions, step-back questions allow us to explore the topic within a wider context.

For example, if the original question is “What are the economic impacts of tourism in Venice?”, the step-back question might be “How does tourism affect local economies in popular cities?”. This shift provides a broader perspective, uncovering patterns and insights that might apply to similar situations beyond the specific case of Venice.

Once the step-back question is generated, we pass both the original and the step-back questions to the LLM. The answers from each are collected and summarized to create a more objective, well-rounded response. This dual-query approach...

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