We wrote a book on LLM system evals with a bear and fox
Hey all! @sridatta and I wrote a book/zine called Forest Friends on system evals for LLM-driven apps. But it's a bit more whimsical, a bit more visual, and very much inspired by the meme of LLMs being a shoggoth polished into a smiley face with RLHF. LLM system evals are important as companies move past the flashy AI demos to reliable production apps. System evals keep coming up as the answer for what you "should do", but it's not exactly a standard part of the software engineering toolkit. So we pulled from @sridatta's seven years as a research engineer at Google, plus a ton of best…
We wrote a book on LLM system evals with a bear and fox launched on October 21, 2024 with 11 votes, #1,114 of 5,185 launches that month and more than 75% of that year's launches.
In plain words
Forest Friends is a visual guide and zine on system evaluations for large language model applications. Created by researchers with experience in AI evaluation, it covers how to test and measure LLM-driven products in production environments. The guide is designed for software teams building reliable AI applications who need practical evaluation methods beyond initial demos. It combines technical expertise with an accessible, illustrated format to explain system evals as a standard engineering practice.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey all! @sridatta and I wrote a book/zine called Forest Friends on system evals for LLM-driven apps. But it's a bit more whimsical, a bit more visual, and very much inspired by the meme of LLMs being a shoggoth polished into a smiley face with RLHF. LLM system evals are important as companies move past the…
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Forest Friends Zine2024 · forestfriends.tech · ▲9Learn to build system evals for your LLM-driven app
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Aiaiai.guide: Plain-English mental model for LLM apps, tools and agentsApr 2026 · aiaiai.guide · ▲7I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
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