| File Name: | Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps |
| Content Source: | https://www.udemy.com/course/complete-rag-bootcamp-build-optimize-and-deploy-ai-apps |
| Genre / Category: | Other Tutorials |
| File Size : | 4.1 GB |
| Publisher: | Data Science Academy |
| Updated and Published: | November 1, 2025 |
Unlock the full potential of Retrieval-Augmented Generation (RAG) — the framework behind today’s 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’ll 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.
What You’ll Learn:
- The core concepts of Retrieval-Augmented Generation (RAG) and why it’s transforming AI.
- 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.
- Building Agentic RAG workflows where intelligent agents plan, retrieve, and reason autonomously.
- Optimizing RAG performance with prompt tuning, top-k selection, and similarity thresholds.
- Adding security, compliance, and role-based governance to enterprise RAG pipelines.
- Integrating RAG into real-world workflows like Slack, Power BI, and Notion.
- Deploying complete front-end and back-end RAG systems using Streamlit and FastAPI.
- Designing evaluation metrics (semantic similarity, precision, recall) to measure retrieval quality.
Tools and Technologies Covered
- LangChain, LlamaIndex, FAISS, OpenAI API, CLIP, Sentence Transformers
- Streamlit, FastAPI, Pandas, Slack SDK, Power BI Integration
- Python, LLM Prompt Engineering, and Enterprise Security Frameworks
Real-World Hands-On Labs
Each section of the course includes interactive labs and Jupyter notebooks covering:
- RAG Foundations – Build your first retrieval + generation pipeline.
- LangChain Integration – Connect document loaders, vector stores, and LLMs.
- Performance Optimization – Hybrid, MMR, and context tuning.
- Deployment – Launch full RAG applications via Streamlit & FastAPI.
- Enterprise Use Cases – Finance, Healthcare, Aviation, and Legal systems.
DOWNLOAD LINK: Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps
Complete_RAG_Bootcamp_Build_Optimize_and_Deploy_AI_Apps.part1.rar – 1000.0 MB
Complete_RAG_Bootcamp_Build_Optimize_and_Deploy_AI_Apps.part2.rar – 1000.0 MB
Complete_RAG_Bootcamp_Build_Optimize_and_Deploy_AI_Apps.part3.rar – 1000.0 MB
Complete_RAG_Bootcamp_Build_Optimize_and_Deploy_AI_Apps.part4.rar – 1000.0 MB
Complete_RAG_Bootcamp_Build_Optimize_and_Deploy_AI_Apps.part5.rar – 72.1 MB
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