Publisher's Synopsis
Harness the potential of Retrieval Augmented Generation (RAG) to build more robust and reliable AI applications. This book provides a comprehensive, hands-on approach to implementing RAG using Python, focusing on real-world NLP and AI use cases. You'll explore:
- The core concepts of RAG and its advantages over traditional language models.
- Practical Python implementations using popular libraries for NLP, vector databases, and large language model APIs.
- Techniques for efficient information retrieval, including semantic search and vector embeddings.
- Strategies for optimizing RAG pipelines for performance and accuracy.
- Applications in question answering, chatbots, document summarization, and more.
Whether you're a seasoned developer or just starting with AI, this book equips you with the knowledge and skills to build powerful, context-aware applications with RAG.
