Item #:
138116994
Unlocking Data with Generative AI and RAG: Enhance generative AI systems by integrating internal data with large language models using RAG
Item #:
138116994
INR 5518
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INR 5518
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What Stands Out
Seamless Integration
Effortlessly combines internal data with large language models, enhancing generative AI applications for more accurate and contextually relevant outputs.
Cutting-Edge Techniques
Utilizes Retrieval-Augmented Generation (RAG) methods to significantly improve the performance of AI systems, addressing common user challenges in data processing and generation.
User-Friendly Guide
Provides clear insights and practical approaches for integrating generative AI with existing data, making it accessible for both technical and non-technical audiences looking to enhance their AI capabilities.
Product Details
| Publisher | Packt Publishing |
| Publication date | 27 Sept. 2024 |
| Language | English |
| Print length | 346 pages |
| ISBN-10 | 1835887910 |
| ISBN-13 | 978-1835887905 |
| Dimensions | 19.05 x 2.01 x 23.5 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to integrate internal datasets with AI models for enhanced insights and analytics.
-
AI Developers
Beneficial for developers aiming to improve generative AI systems with real-time data integration techniques.
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Business Analysts
Great for business analysts who want to leverage AI for better decision-making through enriched data analysis.
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General Audience
Not suitable for casual readers who lack a technical background in data science or AI technologies.
Product Description
Unlocking Data with Generative AI and RAG: Enhance generative AI systems by integrating internal data with large language models using RAG
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English edition Keith Bourne Format: Paperback Editorial Review
Unlocking Data With Generative AI And RAG provides a comprehensive guide to enhancing generative AI systems by integrating internal data with large language models using the Retrieval-Augmented Generation (RAG) framework. The book, published by Packt Publishing, includes 346 pages of valuable insights, making it an essential read for professionals looking to harness the power of AI effectively. With an emphasis on practical applications and innovative methodologies, readers can learn how to apply RAG to improve their AI systems. Additionally, the inclusion of examples and case studies enriches the learning experience, making complex concepts more approachable and applicable in real-world scenarios.
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Pros
- Comprehensive coverage of generative AI concepts
- Practical applications and methodologies included
- Includes examples and case studies
- Well-structured for easy understanding
- Focuses on integrating internal data
Cons
- May require prior knowledge of AI concepts
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