This module is designed to give board directors a clear, strategic understanding of the architecture behind large language models — not at the level of code, but at the level of governance. It equips directors to oversee how architectural decisions shape risks, costs, and long-term enterprise value.

At the data stage, directors learn why scale and quality of training data matter more than sheer volume, and how sourcing and licensing affect compliance and reputation.

In the model architecture stage, attention shifts to the core design of LLMs: transformer networks, attention mechanisms, and parameter size. Directors gain insight into how these architectural choices drive capability — but also dictate energy consumption, financial cost, and risk exposure.

At the inference and deployment stage, the focus is on how models actually deliver value. Boards explore the role of APIs, orchestration frameworks, and retrieval-augmented generation (RAG), along with the governance challenge of monitoring outputs that are probabilistic, not deterministic.

The guardrails and monitoring stage highlights governance in action: the systems that detect bias, enforce safety, and keep costs under control once models are in production.

Finally, in the evolution and retirement stage, directors are shown why LLMs are not static assets. They require periodic retraining, adaptation to new data, and eventually decommissioning or replacement — each with governance implications for risk, cost, and accountability.

In short, the benefit to directors is foresight. This module empowers them to connect architectural decisions to enterprise strategy, ensuring that oversight extends beyond hype into the technical realities that determine whether LLMs create sustainable, ethical, and defensible value.