Building production-ready RAG systems

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

Data Ingestion & Preprocessing, Query Enhancement, Advanced Retrieval & Reranking, Response Synthesis & Prompting, and Performance & Scalability

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, and anyone passionate about cutting-edge AI technologies and looking to apply these techniques in their projects.