Set in Brightwood Forest, a Large Language Model Shoggoth made its home in the canopy. Forest creatures have been using this alien intelligence to answer their questions, tell stories, and even write love letters.

But integrating LLMs into complex applications is not easy: Sometimes the LLM misinterprets their instructions, struggles to understand the data it reads, or chooses to do the wrong thing entirely.

Sometimes it feels like "check the vibes, cross your fingers, and ship it" is the only option, but it's not. We've outlined a more systematic (and more effective!) approach to evaluations, where you:

• Start simple and scale up, so you can begin evaluating immediately without getting tangled in vines
• Use a variety of evaluation techniques, ensuring you always have a way to measure progress
• Design custom metrics that capture what "good" really means for your specific use case
• Create a golden dataset that lets you confidently compare different versions of your system
• Ultimately, transform vague feelings into actionable data, making it easy to improve your LLM implementation

So, grab your solar-powered laptop and find a cozy spot under the bioluminescent mushroom. After reading this enchanted zine, you'll have a toolkit of specific evaluation strategies that you can apply to any LLM-powered system, helping you build with greater confidence and control.