AI Engineering is defined by the theoretical discipline of orchestrating modular components—specifically Large Language Models (LLMs), external tools, and proprietary data—to construct scalable systems that solve business problems rather than training foundational models from scratch. The core mechanism involves integrating Retrieval-Augmented Generation (RAG) architectures to bridge pre-trained model knowledge with domain-specific data via vector embeddings, while employing Model Context Protocol (MCP) frameworks to standardize secure interactions between agents and external sources. This framework operates within the domain of applied artificial intelligence systems engineering, distinguishing itself from traditional data science by prioritizing system composition, latency optimization, cost efficiency, and rigorous operational monitoring over algorithmic derivation.
AI Engineering is defined by the theoretical discipline of orchestrating modular components—specifically Large Language Models (LLMs), external tools, and proprietary data—to construct scalable syste…