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) — 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:

1. RAG Foundations – Build your first retrieval + generation pipeline.

2. LangChain Integration – Connect document loaders, vector stores, and LLMs.

3. Performance Optimization – Hybrid, MMR, and context tuning.

4. Deployment – Launch full RAG applications via Streamlit & FastAPI.

5. Enterprise Use Cases – Finance, Healthcare, Aviation, and Legal systems.

Who This Course Is For

• Developers and Data Scientists exploring AI application design.

• Machine Learning Engineers building context-aware LLMs.

• Tech professionals aiming to integrate retrieval-augmented AI into products.

• 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.