Denis2054/RAG-Driven-Generative-AI

Covers practical implementation of vector embedding pipelines with multimodal data support (text and images), hallucination mitigation through traceable source grounding, and optimization techniques like adaptive RAG and human-in-the-loop refinement. Demonstrates scaling patterns for production RAG systems across multiple vector databases (Pinecone, Chroma, Deep Lake) while balancing fine-tuning trade-offs and implementing knowledge graph visualization for complex data structures.

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