Jev Made Crystal Clear · chapter 2: How Jev Works: State In, Typed Answers Out
Three training paths, redrawn from TypeSafe's AI primer without third-party logos
2026-10-25
RLHF produced chat models and RLVR reasoning models (muted); RLCD, TypeSafe's method, produces calibrated decisions (accent). The RLCD box paraphrases the primer's three-line output contract, quoted in full in the text.
Below: the paragraph from the book that builds this idea, then the diagram itself (Figure 2.3), and a recap. About a minute of reading.
Why would a model's probabilities mean anything? TypeSafe's AI primer answers with a family tree of post-training methods. Pretraining produces a large language model; what you train it on afterwards decides what it is good for. Figure 2.3 redraws the primer's diagram: two branches lead to the chat and reasoning models you already know, and a third leads to Jev.
Recap
- The idea: RLHF produced chat models and RLVR reasoning models (muted); RLCD, TypeSafe's method, produces calibrated decisions (accent).
- The picture: Figure 2.3, from chapter 2 ("How Jev Works: State In, Typed Answers Out") of Jev Made Crystal Clear.
- Go deeper: the chapter builds this step by step, with recipes and sources at the end.
This diagram is one of many in Jev Made Crystal Clear.
Every chapter opens with the gist, draws the hard ideas, and ends with recipes and sources.
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