Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One
Ask your pipeline to “list every exclusion in this policy” and watch what comes back: a clean, confident list of five exclusions, nicely formatted, each one real. Nothing in the answer hints that four are missing, and the user has no reason to double-check a list that looks this tidy. Listing questions break the one assumption retrieval is built on, that the answer is the top passage.
- ▪Ask your pipeline to “list every exclusion in this policy” and watch what comes back: a clean, confident list of five exclusions, nicely formatted, each one real.
- ▪Nothing in the answer hints that four are missing, and the user has no reason to double-check a list that looks this tidy.
- ▪Listing questions break the one assumption retrieval is built on, that the answer is the top passage.
Towards Data Science files mainly under ai. We currently carry 123 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/loop-engineering-for-listing-questions-when-the-answer-is-every-passage-not-the-top-one/ |
| Publication time | Fri, 07 Aug 2026 15:00:00 +0000 |
| Retrieval time | 2026-08-07T15:10:41.704Z |
| Last seen | 2026-08-07T15:10:41.704Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 5xBZ2BHvWYOa · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Large Language Model Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One Enterprise Document Intelligence [Vol.1 #12] – The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them angela shi Aug 7, 2026 23 min read Share Photo by RDNE Stock project, via Pexels. Ask your pipeline to “list every exclusion in this policy” and watch what comes back: a clean, confident list of five exclusions, nicely formatted, each one real. The policy has nine. Nothing in the answer hints that four are missing, and the user has no reason to double-check a list that looks this tidy. Listing questions break the one assumption retrieval is built on, that the answer is the top passage. Here the answer is every passage.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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