This course provides advanced techniques for optimizing Retrieval-Augmented Generation (RAG) systems intended for production deployment. It covers in-depth strategies for enhancing retrieval accuracy, generation quality, system performance, and cost-efficiency. Participants will learn to address complex challenges encountered when scaling RAG solutions and maintaining their effectiveness over time. The material focuses on practical implementation and advanced problem-solving for practical applications.
Prerequisites RAG fundamentals, Python, ML
Level:
Advanced Retrieval Optimization
Implement sophisticated techniques to improve the accuracy and relevance of information retrieval in RAG pipelines.
Generation Fine-tuning and Control
Apply advanced methods for fine-tuning generator models and controlling output quality in production RAG systems.
Performance Engineering for RAG
Analyze and optimize latency, throughput, and resource utilization of RAG systems at scale.
Cost Management Strategies
Develop and implement strategies for managing and reducing the operational costs of production RAG systems.
Advanced Evaluation and Monitoring
Design and implement comprehensive evaluation frameworks and monitoring solutions for RAG systems in production.
Scalability and Reliability Architectures
Architect RAG systems for high availability, fault tolerance, and scalability in demanding production environments.
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