Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems Training Course

Introduction to Retrieval-Augmented Generation (RAG)

• Overview of RAG and its significance for enterprise artificial intelligence applications
• Key components of a RAG system: retriever, generator, and document store
• Comparative analysis with standalone large language models (LLMs) and vector search technologies

Setting Up a RAG Pipeline

• Installation and configuration of frameworks such as Haystack or similar tools for government use
• Document ingestion and preprocessing techniques for optimal data utilization
• Connecting retrievers to vector databases, including FAISS and Pinecone, to enhance retrieval efficiency

Fine-Tuning the Retriever

• Training dense retrievers using domain-specific data to improve relevance for government operations
• Leveraging sentence transformers and contrastive learning methods to refine retrieval accuracy
• Evaluating retriever performance through top-k accuracy metrics to ensure high-quality results

Fine-Tuning the Generator

• Selecting appropriate base models, such as BART, T5, or FLAN-T5, for government-specific tasks
• Instruction tuning versus supervised fine-tuning approaches to optimize model performance
• Utilizing LoRA and PEFT methods to achieve efficient updates and maintain model accuracy

Evaluation and Optimization

• Key metrics for assessing RAG performance, including BLEU, exact match (EM), and F1 scores
• Focusing on latency reduction, retrieval quality enhancement, and minimizing hallucination in generated outputs
• Implementing experiment tracking and iterative improvement processes to continuously refine system performance

Deployment and Real-World Integration

• Deploying RAG systems in internal search engines and chatbots for government applications
• Addressing security, data access, and governance considerations to ensure compliance and integrity
• Integrating RAG with APIs, dashboards, or knowledge portals to enhance operational efficiency

Case Studies and Best Practices

• Real-world applications of RAG in sectors such as finance, healthcare, and legal for government operations
• Strategies for managing domain drift and updating knowledge bases to maintain system relevance
• Future directions and advancements in retrieval-augmented large language model systems for government use

Summary and Next Steps