The core principle defines AI Engineering Systems not merely as model training but as the architectural integration of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and deployment infrastructure into production environments. This theory operates within Computer Science and Software Engineering domains, emphasizing a system-level paradigm where data flow, API orchestration, and vector database management supersede classical machine learning fundamentals in professional application development. The conceptual framework posits that the discipline evolves rapidly due to non-standardized architectures, requiring an abstraction of foundational concepts over transient tools to ensure robustness against frequent shifts in models and platforms.
The core principle defines AI Engineering Systems not merely as model training but as the architectural integration of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, an…