JevMade field notes / Worked recipe

Find a help article without forcing a false match

A search recipe compares keyword matching, Jev choosing from search results, and Jev choosing from a whole small help library.

Original by Jeroen Erne / NexibeoRetrieval

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Credits and license

“10 · Search re-ranking” by Jeroen Erne / Nexibeo. Read the original source.

This expanded guide is an AI-narrated adaptation of the source’s essential explanation, examples and caveats, not a word-for-word reading. The synthetic voice does not imitate the author or imply their endorsement.

License: MIT

MIT License

Copyright (c) 2026 Jeroen Erne (https://nexibeo.com, https://completeaitraining.com)

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Our summary

People often ask for help using different words from the article that answers them. This recipe compares a simple keyword search with two Jev approaches. One gives Jev a shortlist of search results; the other presents the entire small collection.

Jev receives the question and the available articles as possible answers, plus an option for none of them. The author tests all approaches on ordinary support questions, including some the collection cannot answer. This separates finding the closest article from finding one that actually helps.

A second-stage chooser cannot recover an article the first search left out. Reading everything may suit a small collection, but larger libraries face input and option limits. The reported comparison uses a small handmade dataset, so another help centre needs its own evaluation.

Key takeaways

Allow the answer that no article fits.
Check whether the initial search includes the right article.
Test realistic questions before choosing how wide a shortlist should be.

More guides →See the experiments →