The most interesting LLM applications are less a single LLM call and more a complex system that relies on multiple LLM calls. Prompt engineering all these LLM calls explicitly can be painful and time consuming.

DSPy pioneered the idea of "optimization" of LLM systems; using automatic evaluation to optimize the instructions, few shot examples, and even the weights.

LangChain and LangSmith are starting to incorporate some of these in an *online* manner.

This video covers why you should think in terms of LLM systems, why optimization can be useful, what components make the most sense to optimize, and how to use DSPy and LangChain/LangSmith to do so.