“This course contains the use of artificial intelligence”
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
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The core concepts of Retrieval-Augmented Generation (RAG) and why it’s transforming AI.
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Building RAG pipelines from scratch using LangChain, LlamaIndex, and FAISS.
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Implementing hybrid search (keyword + vector) for smarter retrieval.
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Creating multi-modal RAG systems that process text, images, and PDFs.
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Building Agentic RAG workflows where intelligent agents plan, retrieve, and reason autonomously.
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Optimizing RAG performance with prompt tuning, top-k selection, and similarity thresholds.
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Adding security, compliance, and role-based governance to enterprise RAG pipelines.
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Integrating RAG into real-world workflows like Slack, Power BI, and Notion.
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Deploying complete front-end and back-end RAG systems using Streamlit and FastAPI.
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Designing evaluation metrics (semantic similarity, precision, recall) to measure retrieval quality.
Tools and Technologies Covered
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LangChain, LlamaIndex, FAISS, OpenAI API, CLIP, Sentence Transformers
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Streamlit, FastAPI, Pandas, Slack SDK, Power BI Integration
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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:
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RAG Foundations – Build your first retrieval + generation pipeline.
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LangChain Integration – Connect document loaders, vector stores, and LLMs.
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Performance Optimization – Hybrid, MMR, and context tuning.
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Deployment – Launch full RAG applications via Streamlit & FastAPI.
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Enterprise Use Cases – Finance, Healthcare, Aviation, and Legal systems.
Who This Course Is For
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Developers and Data Scientists exploring AI application design.
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Machine Learning Engineers building context-aware LLMs.
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Tech professionals aiming to integrate retrieval-augmented AI into products.
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Students and researchers eager to understand modern AI architectures like RAG.
Outcome
By the end of this course, you’ll 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.
Who this course is for:
- Developers and Programmers who want to integrate Large Language Models (LLMs) with real-time data, APIs, and enterprise workflows.
- Data Scientists and Machine Learning Enthusiasts looking to master embeddings, vector databases, and semantic search for practical AI deployment.
- AI/ML Students and Researchers eager to build a complete RAG-based knowledge assistant project to strengthen their portfolio or academic work.
- Educators and Knowledge Managers interested in automating information retrieval, FAQs, and content summarization within organizations
- Entrepreneurs and Innovators aiming to create AI assistants for business domains — from healthcare to finance, support, or education.
