jev for rag retrieval and groundedness

The Useful Jev Decision

Judge candidate relevance or whether a cited passage supports one extracted claim. Jev should receive only the state needed for that judgment: the query or claim, candidate passage, source metadata and only essential surrounding context. The application defines the possible answers before inference and consumes the typed result; Jev does not own the surrounding workflow.

A verification stage can check each answer claim against its cited passage without asking Jev to write a replacement answer. This is a design pattern, not evidence that every implementation will perform well. Test the exact state, question, model version and action against representative outcomes.

Architecture: Evidence in, Judgment Out, Action in Code

The source record and projected state are not interchangeable. If decisive evidence never enters state, the model cannot recover it; if irrelevant or adversarial text is copied wholesale, it can move the result. Give the projection its own version and test it with the question. The state-design guide shows how to separate trusted fields, untrusted content and derived values.

What Remains in Code

Code retrieves candidates, preserves citations, deduplicates, applies access control and constructs the final answer. Exact checks should run before the model when they can narrow or reject candidates. After Jev returns, code applies thresholds, permissions, fallbacks and the final action. Store the raw distribution rather than only the selected label so later evaluation can distinguish a close decision from a concentrated one.

Version the Complete Decision Instrument

A final label alone is not enough to investigate a regression. The same label can come from a clear distribution or a near-tie, and it may have been produced by a different candidate set or projection. Keep the record append-only so a later threshold change can be replayed without rewriting history. The evaluator scorecard defines validity, robustness, calibration and utility checks for this complete instrument.

How to Evaluate This Use Case

1. Collect representative examples and define the observable outcome before tuning the question.
2. Split broad quality into independently useful Choice, Score or Noul judgments.
3. Pin the Jev model, question, criteria and state projection.
4. Measure class-level errors, calibration, coverage, latency and cost against the simplest baseline.
5. Add review or fallback behavior, then monitor drift and real downstream outcomes.

Run a Decision-Level Baseline Experiment

Compare the Jev design with the simplest credible existing method: deterministic rules, a keyword or embedding classifier, the current LLM prompt, or human-only handling. Hold the evidence unit and outcome definition constant, but allow each method to use its native interface. Include invalid responses, timeouts, retries and review work in the result instead of scoring only successful model calls.

The primary metric should reflect the cost of getting “Judge candidate relevance or whether a cited passage supports one extracted claim.” wrong. Report class or rubric errors by important slice, calibration where the output supports it, automation coverage at the proposed threshold, p50/p95 latency and total workflow cost. Use grouped or time-based splits when related records could leak across train and test. The benchmark methodology gives a reproducible protocol.

Main Failure Mode

Long irrelevant passages hurt accuracy, and community evidence includes a reranking workload where Jev did not beat vector retrieval. Jev 1.13 can also read instructions literally, lose accuracy in irrelevant long state and be steered by adversarial content. Keep state narrow, define boundary cases and never treat model confidence as authorization.