Topic gen ai & application
What happens once a model leaves the notebook: RAG, tool-calling agents, and where humans still belong in the loop.
<- all postsHow JEV Works: Choice, Score, Noul and RLCD
One JEV call takes roughly 70–500 ms. In that window, a request made of state, a model name and typed questions goes in, and a set of probabilities comes out. This post opens each layer of that loop: the schema, the three question types, runnable code, the parallel sampler, RLCD, calibration, how to set thresholds, and the line between what TypeSafe has published and what nobody knows yet.
The JEV Ecosystem: Official SDKs, Community Projects and Open System One Models
A compact list of the tools and repositories in the JEV ecosystem, grouped into three layers by who is responsible: official (the typesafe-ai org), community (calling the real JEV through the SDK or HTTP), and open / self-hosted (open models behind a server that speaks the same protocol). One line per tool: name, link, description.
Decision Models: A New Branch Beside Generative LLMs
Most places we wire an LLM into software only need a decision: which team gets this ticket, should this transaction be blocked, does a person need to look. Yet we still make the model write prose and parse it back. A decision model flips that: declare the answer space first, get back a typed decision with probabilities.
RAG will not save dirty data
Three months debugging an internal QA system, and 80% of the work turned out to be document normalization.
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