Learn to build intelligent, retrieval-powered AI systems using LangChain, LlamaIndex, and real-world RAG workflows
“This course contains the use of artificial intelligence”
What you’ll learn
- Design and Build a Retrieval-Augmented Generation (RAG) System Understand how to integrate large language models (LLMs) with retrieval pipelines.
- Implement Embeddings and Vector Databases for Semantic Search Learn how to generate and store embeddings using tools like OpenAI, ChromaDB, or Pinecone.
- Develop an End-to-End AI Knowledge Assistant Build and deploy a functional AI chatbot using frameworks like LangChain, Streamlit, and FastAPI.
- Evaluate and Optimize AI Performance Metrics Measure your assistant’s accuracy, relevance, and user experience using key performance metrics.
Course Content
- Introduction to Retrieval-Augmented Generation –> 4 lectures • 30min.
- Foundations of RAG Architecture –> 4 lectures • 32min.
- Working with Embeddings and Vector Databases –> 4 lectures • 38min.
- Section 4: Building RAG Pipelines with LangChain –> 4 lectures • 34min.
- Enhancing RAG Performance –> 4 lectures • 36min.
- Deploying RAG Systems –> 4 lectures • 44min.
- Advanced & Hybrid RAG Techniques –> 4 lectures • 1hr 4min.
- Real-World Use Cases –> 4 lectures • 1hr 11min.
- Section 9 –> 2 lectures • 43min.
Requirements
“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:
- 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.
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.