Break Your Own RAG Pipeline Before Users Do
A typical evaluation sends a well-written question to a tidy corpus and checks whether the retriever returns the correct passage.Production document collections rarely stay tidy. Old pages remain searchable after a policy changes. Users type "warehuse" instead of "warehouse." Optical character recognition (OCR), the software that extracts text from scans, may read "SSO" as "SS0." A table can also be divided at a page boundary, separating a number from its label.Each problem can send the retriever to the wrong passage.
- ▪A typical evaluation sends a well-written question to a tidy corpus and checks whether the retriever returns the correct passage.Production document collections rarely stay tidy.
- ▪Old pages remain searchable after a policy changes.
- ▪Users type "warehuse" instead of "warehouse." Optical character recognition (OCR), the software that extracts text from scans, may read "SSO" as "SS0." A table can also be divided at a page boundary, separating a number from its label.Each
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/break-your-own-rag-pipeline-before-users-do/ |
| Publication time | Tue, 22 Sep 2026 15:30:01 GMT |
| Retrieval time | 2026-09-22T15:33:51.028Z |
| Last seen | 2026-09-22T15:33:51.028Z |
| 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 | Q45A2Spc-UMQ · 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 ModelsBreak Your Own RAG Pipeline Before Users DoA small adversarial test set that catches the retrieval failures your evaluation set never willSara NobregaSeptember 22, 20267 min readImage by Author | Claude Design.IntroductionRetrieval-augmented generation (RAG) answers questions using information retrieved from a set of documents. That document collection is called a corpus. A typical evaluation sends a well-written question to a tidy corpus and checks whether the retriever returns the correct passage.Production document collections rarely stay tidy. Old pages remain searchable after a policy changes.
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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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