Parts 1 and 2 of this chapter covered transformer architecture, the SLM/RLM split, context engineering strategies, and the Haystack + LangGraph hybrid architecture. Now Funderburk closes the chapter with two topics that every developer building LLM applications needs to understand: vector stores and the economics of inference.
In Part 1, we covered how transformers work and how models split into small language models (SLMs) and reasoning language models (RLMs). Now Funderburk shifts to a big question: how do you actually interact with these models in a reliable way?
Chapter 2 of Laura Funderburk’s book opens with the big picture of large language models. Where they came from, how they work inside, and where they are heading. If Chapter 1 was about pipelines, this chapter is about the models that sit at the center of those pipelines.