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Duration 14 hours
Course Outline
Overview of Retrieval-Augmented Generation (RAG)
- Definition of RAG and its significance for organizational AI capabilities
- Core components of a RAG architecture: retrieval mechanisms, generative models, and document repositories
- Distinctions between RAG, standalone large language models (LLMs), and standard vector search technologies
Configuration of a RAG Processing Pipeline
- Installation and configuration of frameworks such as Haystack or equivalent solutions
- Processes for data ingestion and document preprocessing
- Integration of retrieval components with vector database solutions (e.g., FAISS, Pinecone)
Optimization of Retrieval Mechanisms
- Training dense retrievers using specialized domain datasets
- Application of sentence transformers and contrastive learning methodologies
- Assessment of retrieval efficacy using top-k accuracy metrics
Optimization of Generative Models
- Selection of foundational models (e.g., BART, T5, FLAN-T5)
- Differentiation between instruction tuning and supervised fine-tuning approaches
- Implementation of LoRA and Parameter-Efficient Fine-Tuning (PEFT) strategies for resource-efficient updates
Performance Evaluation and System Optimization
- Key performance indicators for RAG efficacy (e.g., BLEU, Exact Match, F1 Score)
- Management of latency, retrieval precision, and mitigation of hallucination risks
- Maintenance of experiment logs and continuous iterative improvement
Deployment and Operational Integration
- Implementation of RAG in internal search systems and automated assistance tools
- Addressing security protocols, data access controls, and governance standards
- Integration with application programming interfaces (APIs), analytical dashboards, or institutional knowledge portals
Industry Applications and Operational Best Practices
- Strategic use cases in financial services, healthcare administration, and legal compliance
- Mitigation of domain drift and maintenance of knowledge base currency
- Emerging trends in retrieval-augmented large language model systems
Conclusions and Subsequent Implementation Steps
Requirements
- Foundational understanding of natural language processing (NLP) principles
- Practical experience with transformer-based language models
- Proficiency in Python and basic machine learning development workflows
Target Audience
- NLP engineers
- Knowledge management teams
