“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.

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

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.

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.

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.