{"id":13509,"date":"2026-08-10T11:00:15","date_gmt":"2026-08-10T11:00:15","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-complete-rag-bootcamp-build-optimize-and-deploy-ai-apps-2025-10\/"},"modified":"2026-08-10T11:00:15","modified_gmt":"2026-08-10T11:00:15","slug":"udemy-complete-rag-bootcamp-build-optimize-and-deploy-ai-apps-2025-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-complete-rag-bootcamp-build-optimize-and-deploy-ai-apps-2025-10\/","title":{"rendered":"Udemy \u2013 Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps 2025-10"},"content":{"rendered":"
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Descriptions<\/h2>\n

Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps, This course contains the use of artificial intelligence
Unlock the full potential of Retrieval-Augmented Generation (RAG) \u2013 the framework behind today\u2019s most accurate, data-aware AI systems. This comprehensive bootcamp takes you from the fundamentals of RAG architecture to enterprise-level deployment, combining theory, hands-on projects, and real-world use cases. You\u2019ll learn how to build powerful AI applications that go beyond simple chatbots, integrating vector databases, document retrievers, and large language models (LLMs) to deliver factual, explainable, and context-grounded responses. The course covers building RAG pipelines from scratch using LangChain, LlamaIndex, and FAISS, implementing hybrid search (keyword + vector) for smarter retrieval, creating multi-modal RAG systems that process text, images, and PDFs, and building agentic RAG workflows where intelligent agents plan, retrieve, and reason autonomously. You\u2019ll optimize RAG performance with prompt tuning, top-k selection, and similarity thresholds, add security, compliance, and role-based governance to enterprise RAG pipelines, and integrate RAG into real-world workflows like Slack, Power BI, and Notion. The course also teaches deploying complete front-end and back-end RAG systems using Streamlit and FastAPI, and designing evaluation metrics (semantic similarity, precision, recall) to measure retrieval quality. Each section includes interactive labs and Jupyter notebooks covering RAG foundations, LangChain integration, performance optimization, deployment, and enterprise use cases in finance, healthcare, aviation, and legal systems. By the end of this course, you\u2019ll confidently design, implement, and deploy end-to-end RAG systems, combining the power of LLMs with enterprise data for smarter, explainable, and production-ready AI applications.<\/p>\n

What you\u2019ll learn<\/h3>\n