CommunityJev Guides & Articles
Support ticket classifier
A small support ticket classifier where Jev decides category, urgency and whether a ticket needs a human, replacing a GPT-based version of the same flow.
- Format
- Article
- Published by
- Community
- Last verified
- Oct 2026
Works forJev for Classification
Used inJev for Software & DevTools
Highlights
- Three decisions per ticket: which category it belongs to, how urgent it is, and whether a human is needed.
- The flow was first built with GPT, then re-tested with Jev on the same shape of prompt.
- The test set was a handful of real customer messages, and the result is reported as 100% accurate.
- The cost claim is comparative and unquantified: much less to run than the GPT version.
Watch out
This is a single short post with no repository, no prompt, no question definitions and no category list, so nothing here is reproducible. The headline figure is 100% accuracy on what the author calls a small test of a few real customer messages, which is an observation on a handful of labelled examples rather than a measured accuracy rate, and no ticket count is given. The cost claim, much less to run, carries no figure for either side. The initial GPT version is not described, so it is unclear whether the two runs shared a prompt, labels and retry behaviour, which is the only thing that would make the comparison mean something. Undocumented: how urgency is defined, what the human-versus-auto decision triggers downstream, how low-confidence answers are handled, and whether ticket text is redacted before being sent. Copy reviewed against the linked page on 2026-10-04.
Questions about this entry
Short answers drawn from the linked project or page.
The post says a few real customer messages and calls it a small test, without giving a count. A perfect score on a handful of examples says little about the long tail of real tickets, so read it as evidence the flow runs end to end rather than as an accuracy rate. No ticket count, category list or confusion detail is given.
Unclear. The post says the classifier initially used GPT and was then tested with JEV using the same flow on a few real messages. It does not state whether the prompt, the labels, the retry behaviour or the ticket sample were held constant between the two, so the result comparison rests on an assumption rather than a documented control.
Only that it costs much less than the GPT version, with no numbers on either side and no per-ticket breakdown. Since a ticket plausibly becomes three decisions, a real estimate needs your ticket rate, the prompt length and current TypeSafe pricing, and none of those appear in the post.
Not from this post. There is no repository link, no prompt text, no question definitions and no list of categories or urgency levels. What the post does establish is the shape of the output: category, urgency, and whether the ticket needs a human or can be handled automatically.
Which category the ticket belongs to, how urgent it is, and whether a human is needed. Those three outputs are the shape of the flow the post describes, and it is the same flow that was first built with GPT.
Not documented. The post does not say how urgency is defined, what the human-versus-auto decision triggers on the other side, or how low-confidence answers are handled, so the operational path after classification is invisible.
The post does not say. Whether raw customer messages are redacted before being sent is not addressed anywhere in the capture, which matters because the test used real customer messages rather than synthetic ones.
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