Unlocking Data with Generative AI and RAG
Enhance generative AI systems by integrating internal data with large language models using RAG
Created by Keith Bourne
Explore how to combine large language models with your organization's internal data using Retrieval-Augmented Generation. Gain practical skills in building smarter AI applications that deliver more relevant results and insights. Learn to bridge the gap between cutting-edge AI and real-world business needs.
Packt | Sep 2024 | 346 min
What You Will Learn
You will progress from understanding RAG concepts to hands-on coding with tools like LangChain and Chroma. Through practical examples and real-world case studies, you will see how each technique impacts your AI system's performance. Both technical and non-technical learners will find clear explanations and actionable steps to apply RAG effectively.
Key Features
- Integrate large language models with internal data for smarter AI solutions
- Master vector databases and prompt engineering for precise data retrieval
- Tackle real-world challenges in deploying and optimizing RAG systems
Target Audience
Ideal for AI researchers, data scientists, software developers, and business analysts with a basic grasp of Python and Jupyter Notebooks. If you want to enhance data retrieval, improve AI accuracy, or drive innovation using generative AI, you will benefit from practical examples and strategies tailored to both technical and business-focused roles.
