{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "wh000wh000-awesome-jev-live--ab-test-jev-against-prompt",
 "repo": "wh000wh000/awesome-jev-live",
 "repo_url": "https://github.com/wh000wh000/awesome-jev-live",
 "repo_slug": "wh000wh000-awesome-jev-live",
 "title": "A/B test a typed Jev decision against a prompt",
 "page_title": "How to a/B test a typed Jev decision against a prompt with Awesome Jev Live",
 "summary": "Swap in the official drop-in adapter to measure Jev against an ordinary LLM on your data.",
 "who": "An engineer whose team is deciding whether a typed model call is worth it over a prompt to a general LLM.",
 "problem": "Schema-valid output is not the same as a correct decision, so a team needs its own measurement before committing.",
 "outcome": "You have a side-by-side result on your own examples for Jev and a standard LLM backend.",
 "steps": [
  "Open the system-one-adapter-python entry in the README and read its description: it is a drop-in TypeSafeClient replacement backed by LLM APIs.",
  "Collect a labelled set of real decisions from your own workflow, with the correct answer for each.",
  "Run the same questions through the official TypeSafe client and through the adapter, keeping the answer space identical.",
  "Compare the accuracy of both backends on your labelled set and record where they disagree.",
  "Decide which backend to use based on your own results, not on the README."
 ],
 "agent_prompt": "Using the awesome-jev-live README, locate typesafe-ai/system-one-adapter-python and typesafe-sdk-python. Build an A/B harness on <your labelled dataset> that runs both clients on identical typed questions. Output an accuracy table per backend and verify that both return the same answer type.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "The README says to calibrate Jev on your own data, so a comparison on public examples will not settle the question for your use case.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "A TypeSafe API key, which the official SDK description says is the starting point for a typed decision",
  "Python or Node.js/TypeScript, depending on the official SDK you choose",
  "A bounded decision you want to make with typed answers, such as routing, triage, scoring or a yes/no gate"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/wh000wh000-awesome-jev-live/ab-test-jev-against-prompt/",
 "md": "https://dedupedai.com/use-cases/wh000wh000-awesome-jev-live/ab-test-jev-against-prompt/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/wh000wh000-awesome-jev-live--ab-test-jev-against-prompt.json",
 "api_version": "v1"
}