Finetuning: The Theory

In Chapter 4, you learned about making your LLM-powered application safer and more controllable. In particular, you focused on using NeMo to build guardrails around ensuring your LLM stays on topic, executes the right flow, and is able to block users. You looked into NeMo and understood how it combines LLMs, Colang, and embedding models to create a generalized set of rules, based on natural language rules you give it.