Building production-ready RAG systems

What to Expect:

• A deep dive into building, optimizing, and scaling Retrieval Augmented Generation (RAG) systems for real-world applications.

What You Will Learn:

• 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.

Who Should Attend:

• 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.