RAG-Driven Generative AI: Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone

ABSTRACT

RAG-Driven Generative AI provides a detailed roadmap for designing, managing, and controlling multimodal AI pipelines that balance high performance with API cost efficiency. It offers deep explorations into building customized RAG frameworks, covering essential techniques like chunking, indexing, and ranking via Pinecone, Deep Lake, and LlamaIndex. The author expertly guides readers through minimizing AI hallucinations by linking each response directly to traceable source documents, establishing a highly reliable system for grounded, enterprise-ready intelligence.