Duration: 3 Days

Description

This course dives deep into Advanced Retrieval-Augmented Generation (RAG) techniques, equipping participants with the knowledge and skills to enhance language model performance by integrating external knowledge retrieval mechanisms. Over three days, participants will explore advanced architectures, retrieval techniques, optimization strategies, and real-world applications of RAG models. The course combines theory, case studies, and hands-on experience, making it ideal for AI professionals seeking to improve RAG systems for tasks like question answering, chatbots, and document generation.

Audience

This course is designed for experienced machine learning engineers, AI researchers, and technical professionals who already have a foundational understanding of natural language processing and want to deepen their expertise in advanced Retrieval-Augmented Generation (RAG) systems. Ideal participants are those working on or planning to build scalable, high-performance AI applications that require real-time or domain-specific knowledge integration, such as enterprise search, automated support, or document generation tools.

Objectives

  • Learn advanced RAG architecture and retrieval strategies for LLMs
  • Implement optimized knowledge retrieval with vector databases and APIs
  • Fine-tune RAG models for enhanced performance and domain-specific tasks
  • Design scalable RAG pipelines for production use cases
  • Address challenges such as latency, accuracy, and retrieval relevance in RAG

Prerequisites

Participants should have at least 6 months of hands-on Python programming experience, familiarity with large language models (LLMs) and transformer architectures, experience with information retrieval and natural language processing (NLP) associations, and basic knowledge of vector databases (e.g., FAISS, Pinecone).

Course Outline

Module 1: Overview of Retrieval-Augmented Generation (RAG)

  • What is RAG?
  • Use Cases and Benefits of RAG
  • Overview of Traditional RAG Architecture

Module 2: Advanced Retrieval Techniques

  • Dense vs. Sparse Retrieval Methods
  • Using Vector Databases
  • Introduction to Embeddings

Module 3: Enhancing the Retrieval Process

  • Integrating Multiple Retrieval Methods
  • Multi-Stage Retrieval
  • Engineering Custom Retrievers

Module 4: Building an Advanced RAG Pipeline

  • Setting Up a RAG Pipeline
  • Fine-Tuning Retrieval Strategies
  • Testing Retrieval Efficiency

Module 5: Fine-Tuning Pre-Trained RAG Models

  • Overview of Pre-Trained RAG Models
  • Steps to Fine-Tune RAG for Domain-Specific Tasks
  • Dataset Preparation

Module 6: Optimizing RAG for Latency and Relevance

  • Addressing Latency Issues in Real-Time Retrieval
  • Improving Retrieval Relevance through Reranking Strategies
  • Techniques for Reducing Hallucination in Generated Outputs

Module 7: Enhancing Generation Quality with RAG

  • Techniques for Controlling Generation
  • Improving Knowledge-Grounding Mechanisms in Generated Text
  • Handling Long Documents

Module 8: Fine-Tuning a RAG Model

  • Fine-Tuning a Pre-Trained RAG Model on a Custom Knowledge Base
  • Testing the Model’s Performance for Question Answering or Document Generation
  • Implementing and Evaluating Retrieval Relevance and Accuracy

Module 9: Scaling RAG Systems for Production

  • Designing Scalable RAG Pipelines for Large-Scale Applications
  • Integrating RAG with Cloud-Based Databases and Distributed Systems
  • Handling High-Volume Queries

Module 10: RAG in Real-World Applications

  • Case Studies: Customer Support, Search Engines, and Virtual Assistants
  • Using RAG for Document Generation, Summarization, and Research
  • Advanced Use Cases: Real-Time Retrieval with Streaming Data

Module 11: Monitoring and Maintaining RAG Systems

  • Monitoring Retrieval Quality and Model Performance in Production
  • Continuous Retraining and Updates
  • Debugging and Maintaining Large-Scale RAG Systems

Module 12: Deploying and Scaling a RAG Pipeline

  • Setting Up a RAG System with a Cloud-Based Vactor Database
  • Scaling the System for Real-Time Queries
  • Monitoring System Performance and Optimizing for High Throughput