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After the basics · Worked guides

Jev reranking and line search: candidates before answer locations

Use an offline PDF-export query to separate retrieval, reranking, line selection, and answer availability.

Source checked 2026-10-0217 minutes
What you will build

Keep a fixed candidate set, score it consistently, and locate exact evidence lines.

Before you start · Jev for RAG: relevance, evidence support, and citation checks

Design it step by step

01

Define the answer requirements

Invented query: “How do I export a project to PDF without internet?” Evidence must cover offline operation, format, and steps. A PDF overview is not an offline-export answer.

02

Retrieve a bounded shortlist first

Use keywords, embeddings, or both, with product and version filters. An illustrative ten-passage cap needs recall validation. Reranking cannot recover a document absent from its input.

03

Use one rubric for every pair

Example Score levels: 0=irrelevant, 1=format only, 2=export without offline coverage, 3=offline PDF steps. Save IDs and raw judgments. Ranking priority does not guarantee completeness.

04

Locate lines after selecting passages

Give source lines stable IDs. Choice can select a line or none; fetch neighboring context as needed. Copy original text by ID rather than rewriting commands or button labels.

05

Compare on fixed labeled queries

Measure needed-evidence retrieval, first useful result, and correct no-answer behavior. Moving invented candidate C from third to first improves that example only. Test missing answers, version mismatches, and cross-language queries.

Worked example · Invented by this site

ObservationJudgmentApplication action
A: PDF overviewRelated topic, no stepsDo not use as an offline answer
B: cloud exportRequires internetPreserve the conflicting condition
C: offline PDF stepsCovers the requirementsFetch evidence lines and limits

A copyable design draft

Original examples. JSON illustrates request or input structure; Python calculates invented scores without an API call. Verify current interfaces and task policy before integrating.

# Offline ranking exercise: invented scores.
ratings = {"A": 1, "B": 2, "C": 3}
ranked = sorted(ratings, key=lambda item: (-ratings[item], item))
print(ranked)  # ['C', 'B', 'A']
# Ranking changes order; it does not verify answer support.
Design a decision

Common mistakes

  1. Treating reranking as corpus retrieval.
  2. Changing the rubric between candidates.
  3. Assuming a similar line contains the answer.

TRY / THINK / COMPARE

Think first, then compare

The correct page is missing from the top ten. Tune only the reranker threshold?

Show explanation

No. Inspect retrieval and filters first; reranking cannot restore missing input.

Before handing it over

  • Queries, candidates, and labels are fixed.
  • All candidates share a rubric.
  • Line IDs resolve to original versions.
  • Missing answers and conflicts have fallbacks.

Common questions

Does line search replace document context?

No. Read the surrounding scope and conditions.

Can the top-ranked passage answer automatically?

Check coverage, conflict, and missing facts separately from rank.

Sources and further reading

Inspired by official patterns and Datawhale practice topics. Explanations, examples, and exercises are independently written. These are teaching designs, not live API runs or benchmarks.