Systems & Methods

Systems and methods are the technical choices that shape an AI system before it reaches a user: architecture, training, data handling, interfaces, evaluation and deployment. A method name is useful context, but it is not by itself an explanation of capability, reliability or suitability.

The Transformer paper introduced an architecture based solely on attention mechanisms, replacing the recurrent and convolutional layers used in earlier sequence-transduction approaches.¹ Its influence makes architecture an important part of modern AI literacy, while still leaving many implementation and operational choices to examine.

Methods live in a wider system

The model is only one component. Retrieval, tools, prompting, access controls, monitoring and human review can materially change how a deployed system behaves. That means a statement about a method should identify the surrounding system and the task it was tested on whenever the evidence makes that possible.

NIST’s Generative AI Profile frames generative-AI risk through the lifecycle and context in which a system is designed, developed, deployed and used.² This supports a systems view: technical mechanisms and operational controls need to be assessed together.

What this topic covers

This hub maps core architectures, training and inference approaches, retrieval and tool-use patterns, and the deployment methods that make those components useful—or risky—in practice.

Footnotes

1. Vaswani et al., Attention Is All You Need — full source details. ↩

2. NIST, Generative AI Profile — full source details. ↩