AIMLverse Lab

Retrieval-Augmented Generation (RAG) in AI/ML

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About Course

Description:
Dive into the cutting-edge world of Retrieval-Augmented Generation (RAG), a powerful hybrid architecture combining the strengths of retrieval-based systems and generative models. In this course, you’ll learn how RAG enhances language models by integrating external knowledge sources, enabling more accurate and context-aware responses. Through practical projects and hands-on coding, you’ll build RAG pipelines using tools like Hugging Face Transformers, FAISS, and LangChain, and apply them in real-world scenarios such as question answering, chatbots, and document search.

Key Topics:

  • Fundamentals of RAG architecture

  • Vector stores and dense retrieval (e.g., FAISS, ChromaDB)

  • Integrating retrieval with generative transformers

  • Building end-to-end RAG systems using Python

  • Use cases in search, Q&A, and legal/enterprise AI

Prerequisites:
Basic understanding of Python, NLP, and transformer-based models.

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What Will You Learn?

  • ✅ Understand the core concepts of Retrieval-Augmented Generation (RAG) and how it improves traditional language models
  • ✅ Implement document retrieval systems using vector databases like FAISS and ChromaDB
  • ✅ Generate high-quality embeddings using state-of-the-art models (e.g., BERT, OpenAI, Hugging Face models)
  • ✅ Build and fine-tune RAG pipelines using tools like Hugging Face Transformers and LangChain
  • ✅ Integrate retrieval and generation components for custom Q&A and chatbot systems
  • ✅ Apply prompt engineering techniques to improve LLM responses in RAG setups

Course Content

Module 1: Introduction to RAG and Generative AI
What is Retrieval-Augmented Generation (RAG)? Limitations of standalone LLMs Benefits of combining retrieval with generation Real-world use cases (Chatbots, Q&A, Legal AI, etc.)

Module 2: Foundation of NLP and Transformers (Refresher)
Text embeddings and vector representations Overview of Transformers and pretrained language models Understanding Hugging Face ecosystem

Module 3: Document Retrieval Techniques
Classical vs Neural retrieval Dense vector stores (FAISS, ChromaDB, Weaviate) Creating and indexing document datasets Text chunking, metadata, and preprocessing strategies

Module 4: Building the Retrieval Pipeline
Generating embeddings using SentenceTransformers, OpenAI, or Hugging Face models Indexing documents with FAISS/Chroma Querying vector stores and ranking results Evaluation: Precision, Recall, and MRR

Module 5: Combining Retrieval with Generation
Understanding the RAG architecture Hugging Face RAG implementation Custom RAG pipeline with LangChain Prompt Engineering for improved generation