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Course Outline
Introduction to Retrieval-Augmented Generation (RAG)
- An overview of RAG and its significance for government AI initiatives
- Key components of a RAG system: retriever, generator, and document store
- A comparison with standalone language models and vector search methods
Setting Up a RAG Pipeline
- Installing and configuring frameworks such as Haystack or similar tools for government use
- Document ingestion and preprocessing techniques tailored for government data
- Connecting retrievers to vector databases, including options like FAISS and Pinecone, to enhance retrieval efficiency
Fine-Tuning the Retriever
- Training dense retrievers using domain-specific data relevant to government operations
- Leveraging sentence transformers and contrastive learning methods for improved performance
- Evaluating retriever quality through top-k accuracy metrics to ensure reliable information retrieval
Fine-Tuning the Generator
- Selecting appropriate base models, such as BART, T5, or FLAN-T5, for government applications
- Choosing between instruction tuning and supervised fine-tuning based on specific use cases
- Utilizing LoRA and PEFT methods to efficiently update models while maintaining performance standards
Evaluation and Optimization
- Metric selection for evaluating RAG performance, including BLEU, EM, and F1 scores, to ensure alignment with government standards
- Focusing on latency reduction, retrieval quality, and minimizing hallucinations in generated content
- Implementing experiment tracking and iterative improvement processes to continuously enhance system performance
Deployment and Real-World Integration
- Deploying RAG in internal search engines and chatbots for government agencies
- Addressing security, data access, and governance considerations specific to the public sector
- Integrating RAG with APIs, dashboards, or knowledge portals to support government workflows
Case Studies and Best Practices
- Exploring enterprise use cases in finance, healthcare, and legal sectors for government applications
- Strategies for managing domain drift and updating knowledge bases in a government context
- Future directions and advancements in retrieval-augmented language model systems for government use
Summary and Next Steps
Requirements
- An understanding of natural language processing (NLP) concepts for government
- Experience with transformer-based language models
- Familiarity with Python and basic machine learning workflows
Audience
- NLP engineers for government
- Knowledge management teams for government
14 Hours
