Use cases· Last updated 2026-09-21

RAG production rollout with Jev

Retrievers hope. After retrieval, Jev marks relevance, contradiction, or injection; code keeps, flags, or drops passages before a generator sees them.

This unofficial page is the production rollout slice of the RAG passage decisions pack. Intent: apply the Jev (TypeSafe System One) decision model to RAG passage decisions production rollout. Primary search language: RAG Jev production rollout. Confirm patterns on docs.typesafe.ai. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Independent angle (cover ≠ clone): Filter/rerank recipes with failure modes; cite vs generate boundary. We cover the intent, not a rival rerank-passages-score URL tree.

RAG use-case context

Production rollout for RAG passage decisions is Operate-pillar work: pin, shadow, canary, abort. It is not a launch-checklist clone of a rival “build with System One” guide — we only talk about this pack’s blast radius (showing a passage to a customer-facing answerer).

Hub: Classifying RAG passages. Compare, when the other tool is the real job: RAG pipelines.

Production Rollout inputs

Ship the contracted payload, the pinned id, and a documented safe default when the API is unavailable:

{
  "query": "What is the refund window for pro plans?",
  "passage": { "id": "doc-88#p3", "text": "Pro subscribers may request a refund within 14 days." },
  "corpus": { "trust": "internal_kb" }
}

Decision signals and actions

Keep the workflow code you already designed:

def keep(ans, passage_id):
    if ans["injection"].noul >= T_INJECT:
        return "drop_security"
    if ans["relevant"].noul < T_REL:
        return "drop_irrelevant"
    return "keep"

Do not treat a Noul of 0.5 as a “medium” RAG passage decisions score — it means yes and no are equally likely. Conjunctions stay in your code.

Guardrails and escalation

Safe default if System One errors or confidence is low: do not feed the passage to the customer-facing answerer. TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For RAG passage decisions, treat feed_to_answerer as the high bar (showing a passage to a customer-facing answerer). Tune on labels — see offline evaluation.

Evaluation and rollout notes

Promotion gate = evaluation green on the pinned id + audit traces for the canary. Pin jev-1.13.0 (the versioned id) after you fit thresholds. jev-latest and the marketing line jev-1.13 can move. Log the response model. TypeSafe’s published list price for jev-1.13 is $0.042 per million input tokens (vendor claim — confirm on the models page); output tokens are free on that same page. Unused distractors still bill as input.

Official Python and JavaScript SDKs read TYPESAFE_API_KEY and retry documented 429/529. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Pack map

FAQ

Can I ship on jev-latest? Pin jev-1.13.0 (the versioned id) after you fit thresholds. jev-latest and the marketing line jev-1.13 can move. Log the response model. Shadow first.

What is the safe default if System One is down? Fail closed on showing a passage to a customer-facing answerer. Do not guess.

Where is the rest of the RAG pack? Start with RAG evaluation and RAG confidence thresholds. Cluster hub: Use cases.

Should Jev generate the RAG answer? No. Classify or score passages; another model (or extractive code) writes. That is the cite-vs-generate boundary.

Do we publish rerank lifts? No. TypeSafe’s cookbooks may show measurements — treat those as vendor figures and re-run on your corpus.

What this page does not claim

• No invented top-1 / top-10 lifts.
• Not a vector database.
• Not official TypeSafe.
• Official TypeSafe status, or that jev.pro issues API keys.
• That a schema-constrained answer is automatically factually correct.

Disclaimer

This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai.

Primary documentation: https://docs.typesafe.ai. Hub: Use cases.