Machine Learning Engineering

RAG architecture connects a generative AI model to external knowledge. The system retrieves relevant documents or passages, adds them to the model’s context, and uses that evidence to generate a response.

In this article, it is explained how a RAG system works in practice, from document ingestion and embeddings to semantic search, context selection, generation, and evaluation.

This article demystifies the three primary model families used in business forecasting regression, time series, and ensemble methods explaining what each does, when it works, and when to look elsewhere.