07-07, 13:30–17:30 (US/Pacific), Ballroom C
Large Language Models (LLMs) have revolutionized natural language processing, but they come with limitations such as hallucinations and outdated knowledge. Retrieval-Augmented Generation (RAG) is a practical approach to mitigating these issues by integrating external knowledge retrieval into the LLM generation process.
This tutorial will introduce the core concepts of RAG, walk through its key components, and provide a hands-on session for building a complete RAG pipeline. We will also cover advanced techniques, such as hybrid search, re-ranking, ensemble retrieval, and benchmarking. By the end of this tutorial, participants will be equipped with both the theoretical understanding and practical skills needed to build robust RAG pipeline.
RAG is a rapidly growing field with practical applications in AI-powered search, chatbots, and domain-specific knowledge retrieval. This tutorial provides a structured, hands-on learning experience for participants and implement more reliable and context-aware AI systems.
Target Audience:
- Data scientists and ML practitioners working with LLMs.
- Engineers building AI-driven search and retrieval applications.
Expected Outcomes:
- Understand the role of retrieval in improving LLM performance.
- Implement a functional RAG pipeline using open-source tools.
- Learn advanced retrieval and ranking techniques.
- Gain insights into scaling RAG for production use cases.
Requirements:
- Familiarity with Python and basic NLP concepts.
- Laptop with Python 3.8+, Jupyter Notebook, and required libraries installed.
- Access to an LLM API (e.g., OpenAI, Llama 2).
Outline:
Part 1: Introduction to RAG
- Overview of Retrieval-Augmented Generation.
- Why retrieval is essential for LLMs.
- Real-world applications and use cases.
Part 2: Breaking Down the RAG Pipeline
- Key components of a RAG system:
- Document ingestion and chunking.
- Embedding models and vector databases.
- Retrieval strategies: BM25, dense retrieval, hybrid search.
- Response generation with LLMs.
- Trade-offs between different retrieval methods.
Part 3: Hands-on Implementation
- Setting up a basic RAG pipeline with LangChain and FAISS.
- Implementing hybrid retrieval for better search results.
- Evaluating retrieval and generation quality.
Part 4: Advanced RAG Techniques
- Re-ranking retrieved documents.
- Combining multiple retrievers (ensemble retrieval).
Requirements
- Familiarity with Python and basic NLP concepts.
- Laptop with Python 3.8+, Jupyter Notebook, and required libraries installed.
- Access to an LLM API (e.g., OpenAI, Llama 2).
I am a Senior Manager in Data Science at Capital One, specializing in machine learning, generative AI, and Retrieval-Augmented Generation (RAG). I am passionate about leveraging AI to solve complex business challenges and drive innovation.
Data Scientist at Capital One
Data Scientist at Capital One
