Learn how to monitor and evaluate a production-grade RAG pipeline with retrieval metrics, faithfulness, observability, latency, cost, regression testing, and user feedback.
Prepare for RAG system interviews with practical questions on architecture, chunking, embeddings, vector databases, retrieval, reranking, evaluation, security, latency, cost, and production system design.
Retrieval-Augmented Generation (RAG) has become one of the most important architectures for building production-grade Generative AI applications. From enterprise chatbots and document Q&A systems to AI search engines and knowledge assistants, RAG allows Large Language Models (LLMs) to generate responses using external, up-to-date, and domain-specific information. If you are preparing for an AI Engineer, Machine … Read more