2 Foundation Models: Language & Embedding
This chapter covers
- What foundation models are and how they became the new standard in AI
- The architecture and training pipeline behind models like GPT and Claude
- How inference and reasoning strategies affect model behavior
- Why embeddings matter—and how they power search, retrieval, and recommendations
- Key trade-offs: hallucinations, bias, compute cost, and deployment decisions.
This chapter explains the engineering foundations behind modern AI systems—what they are, how they’re built, and why they behave the way they do. If you want to move beyond simply calling an API and start making informed decisions about how models are used in your applications, this is where it begins.
We’ll walk through the full development pipeline of foundation models like GPT-4 or Claude, from large-scale pretraining on raw web data to post-training alignment methods like instruction tuning and RLHF. You’ll learn how inference parameters affect outputs, and why small changes in temperature or stop sequences can drastically shift a model’s behavior.
Alongside language models, we’ll introduce embedding models—less visible, but essential to systems like semantic search and Retrieval-Augmented Generation (RAG). These models don’t generate text; instead, they map meaning into vectors, enabling machines to organize and retrieve information based on semantic similarity.