Models are the engine: Output defines the destination (Component 1), Input is the fuel (Component 2); this component is about installing and tuning that engine.
Pre-training a general-purpose foundation model from scratch is a poor fit for almost every organization. The higher-impact work is calibrating strong pre-trained models to your domain.
Chapter 7 connects data to performance: especially context engineering, putting structured and unstructured data in prompt templates for consistent
behavior, and when context hits limits, moving to fine-tuning with data.
Chapter 8 covers choosing and configuring models: the intelligence, speed, and cost trade space, plus practical configuration and optimization
options.
Goal: models feel like engineerable components, not untouchable black boxes, ready to integrate for impact.
Framing (from the component introduction)
Modern foundation models offer multimodal inputs, hyperparameters, and many sizes, with complexity that emerged from competitive innovation, analogous to a mature combustion engine.
Read the chapter for…
The car-engine analogy, graphics, and full technical depth in Chapters 7 and 8 on context engineering, tuning, selection, and provider tradeoffs.