Bridge Knowledge and Language—Build Smarter AI with Retrieval-Augmented Generation
Building RAG Pipelines with LLMs is a specialized, project-based course that teaches how to combine the power of Large Language Models (like OpenAI GPT, Cohere, Claude, and Llama) with custom knowledge sources through Retrieval-Augmented Generation. RAG enhances AI responses by grounding them in factual, external data—making this a must-learn skill for developers, researchers, and innovators working in knowledge-intensive domains like legal tech, finance, healthcare, and education.
To provide participants with practical skills and technical knowledge to design, build, and deploy Retrieval-Augmented Generation (RAG) pipelines using Large Language Models (LLMs) for accurate, context-aware AI applications.
To train participants in the practical construction of RAG architectures
To deepen understanding of how LLMs interact with external data
To empower learners to build scalable, accurate, and context-aware AI systems
To prepare professionals for high-demand GenAI engineering roles
Week 1: Foundations of Retrieval-Augmented Generation
Module 1: Introduction to RAG Systems
Chapter 1.1: What is Retrieval-Augmented Generation?
Chapter 1.2: Components of a RAG Pipeline
Chapter 1.3: Benefits and Limitations of RAG
Module 2: Understanding Retrieval and Vector Databases
Chapter 2.1: Dense vs. Sparse Retrieval
Chapter 2.2: Vector Embeddings and Semantic Search
Chapter 2.3: Overview of Tools (FAISS, Weaviate, Pinecone, Qdrant)
Week 2: Building the Core RAG Stack
Module 3: Integrating LLMs with Search
Chapter 3.1: Embedding Generation (OpenAI, Hugging Face)
Chapter 3.2: Chunking and Preprocessing Strategies
Chapter 3.3: Prompt Templates for RAG
Chapter 3.4: Connecting LLMs to Vector DBs
Module 4: RAG System Implementation
Chapter 4.1: Document Ingestion and Indexing
Chapter 4.2: Query Handling and Retrieval Flow
Chapter 4.3: Response Synthesis using LLMs
Chapter 4.4: Evaluation Metrics for RAG Responses
Week 3: Optimization, Deployment, and Projects
Module 5: Advanced RAG Techniques
Chapter 5.1: Hybrid Search (BM25 + Embeddings)
Chapter 5.2: RAG with Structured and Unstructured Data
Chapter 5.3: Multi-turn and Conversational RAG
Module 6: Deployment and Capstone
Chapter 6.1: Deploying RAG Systems with LangChain or LlamaIndex
Chapter 6.2: Monitoring, Caching, and API Design
Chapter 6.3: Capstone Project – Build Your Own RAG Pipeline
Chapter 6.4: Industry Use Cases and Future Trends
Developers, data scientists, researchers, and AI engineers
Graduate students and working professionals in AI/ML or NLP
Prior experience with Python and APIs is recommended
Understand how to build and optimize a complete RAG pipeline
Work with real-world data to build question-answering systems
Integrate vector databases with LLM APIs
Design scalable, domain-specific GenAI applications
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
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