AED
UAD Dirham
Code: AED
AI · Moderate · Live online
Building RAG Pipelines with LLMs
Bridge Knowledge and Language—Build Smarter AI with Retrieval-Augmented Generation
★ 4.2 (28 ratings)3 WeeksCertificate
About This Course
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.
Aim
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.
Program Objectives
-
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
Program Structure
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
Who Should Enrol?
-
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
Program Outcomes
-
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
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
Frequently asked questions
Will I get the recording?
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
What if I miss a live session?
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Do I get a certificate?
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Which fee category applies to me?
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy