Gen AI → RAG (Retrieval Augmented Generation) Architecture Problems Solutions by RAG, Data Embedding

Gen AI → RAG (Retrieval Augmented Generation) RAG Architecture Ingestion Pipeline (Data → Embeddings → Vector DB) Retrieval (Query → Embeddings → Similarity Search) Generation (LLM uses retrieved documents) Problems Lack of private knowledge Hallucinations Inability to answer domain-specific queries Outdated knowledge Solutions by RAG Use your own data without training Up-to-date dynamic information Reduced hallucinations Data privacy ==================================================== मन के भाव ? Who Am I मैं कौन हूँ: https://www.youtube.com/@feelingsOfminds/videos Knowledge Sharing YouTube: https://www.youtube.com/@KnowledgeSharingG/videos Knowledge Sharing Blog: https://depthinjava.blogspot.com/ Stock Market YouTube: https://www.youtube.com/@stocksmarketupdates/videos Stock Market Blog: https://stocksmarketupdates.blogspot.com/ LinkedIn: https://www.linkedin.com/in/ajay-nirankari-a7537140/ Facebook: https://www.facebook.com/ajaynirankari Twitter: https://twitter.com/AjayNirankari GitHub: https://github.com/ajaynirankari Telegram: https://t.me/BasicKnowledgeSharing ==================================================== Knowledge Sharing Knowledge Sharing KnowledgeSharing KnowledgeSharingG #Knowledge Sharing #Knowledge #Sharing #KnowledgeSharing #KnowledgeSharingG