RAG evaluation 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 evaluation slice of the RAG passage decisions pack. Intent: apply the Jev (TypeSafe System One) decision model to RAG passage decisions evaluation. Primary search language: RAG Jev evaluation. 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.
Score Precision@k of kept passages and injection-canary drop rate — not claim-support gold (that is citation eval).
RAG use-case context
Evaluation for RAG passage decisions is a frozen harness, not a vibe check and not an opinion-blog “Jev review.” Labels: passage relevant / not, plus injection gold on a hostile slice. We publish no unofficial accuracy.
Hub: Classifying RAG passages. Compare, when the other tool is the real job: RAG pipelines.
Evaluation inputs
Replay the same contract you ship:
{
"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" }
}Freeze questions, criteria, and jev-1.13.0. Record the response model.
Decision signals and actions
Score these, not a blog-grade star rating:
- Precision@k of kept passages vs your labels
- Generator groundedness after the filter (your harness)
- Drop rate on an injection-canary set
Pair auto-act errors with handoff rate. If the retrieval filter never acts, you have not evaluated RAG passage decisions — you have evaluated a human queue.
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
Promote a threshold only when the harness says auto-act error ≤ SLA and reviewers still catch the residual. 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
After any criteria edit, rerun before production. Cookbook lifts you see on TypeSafe pages are vendor claims — re-measure on your query + retrieved passages. 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
| Slice | Page |
|---|---|
| Graph and primitives | decision workflow |
What may enter state |
input contracts |
| What to gather first | evidence collection |
| Atomic rules | policy checks |
| Act / review / abstain | confidence thresholds |
| Reviewer payload | human handoff |
| What to persist | audit trail |
| How it breaks | failure modes |
| Labeled replay | you are here |
| Shadow → canary | production rollout |
FAQ
Will jev.pro publish a leaderboard for this use case? No. Measure on your labels. Vendor cookbook figures stay labeled as vendor claims.
What must stay frozen? Questions, criteria, and the pinned model id. Aliases can move.
Where is the rest of the RAG pack? Start with RAG failure modes and RAG production rollout. 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.
Sources
Public TypeSafe or adjacent documentation only. No private claims.