- Data Ingestion & Preprocessing: Learn efficient strategies for handling diverse data sources, optimizing chunk size, and leveraging metadata for improved retrieval.
- Query Enhancement: Master techniques for understanding user intent, extracting keywords, and decomposing complex queries for better retrieval results.
- Advanced Retrieval & Reranking: Explore techniques for managing LLM context length, mitigating hallucination, and optimizing retrieval performance for various use cases.
- Response Synthesis & Prompting: Develop effective prompting strategies, implement guardrails, and optimize for accurate and relevant responses.
- Performance & Scalability: Learn how to optimize pipelines for efficiency and reduce the number of LLM calls for cost optimization. Discover strategies for parallelization and scaling your RAG system effectively.
- Machine Learning Engineers and Data Scientists working on or interested in RAG systems.
- AI Practitioners seeking practical insights and solutions for real-world deployment.
- Product Managers and Tech Leads focused on integrating advanced AI systems into production environments.
- Anyone passionate about cutting-edge AI technologies and looking to apply these techniques in their projects.
WHAT TO EXPECT
A deep dive into building, optimizing, and scaling Retrieval Augmented Generation (RAG) systems for real-world applications.
WHAT YOU WILL LEARN
WHO SHOULD ATTEND