Data Machina #245

Substack · 2 years ago · 44

Retrieval-Augmented Generation (RAG) systems have evolved significantly since Facebook AI introduced the technique three years ago, with practitioners now moving toward modular architectures that balance scalability, cost-efficiency, and accuracy. Key developments include Cohere's Command-R model optimized for production-scale RAG, Weaviate's open-source Verba system, and new techniques like RAFT that combines RAG with domain-specific fine-tuning, alongside improvements addressing limitations of cosine-similarity matching and integrations with knowledge graphs. These advances aim to address the practical challenges that have prevented many enterprise RAG projects from reaching production, where naive and advanced RAG implementations often failed to deliver reliable results at manageable costs.