Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination
The article discusses the concept of a typed answer contract in large language models to prevent hallucination. This contract is a schema that the model must fill, with every field being a question that the pipeline asks the model and every answer being checkable. The schema is the contract between the pipeline and the model, and it can be extended to ask for more than just the answer, including typed values, multi-element answers, and citations.
- ▪The typed answer contract is a schema that the model must fill, with every field being a question that the pipeline asks the model and every answer being checkable.
- ▪The schema is the contract between the pipeline and the model, and it can be extended to ask for more than just the answer.
- ▪The model's job is to turn passages and a question into an answer, and it will hallucinate if it is not grounded in the retrieved passage.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/stop-returning-text-from-rag-the-typed-answer-contract-that-prevents-hallucination/ |
| Publication time | Sat, 04 Jul 2026 13:00:00 +0000 |
| Retrieval time | 2026-07-04T13:15:43.738Z |
| Last seen | 2026-07-04T19:00:18.637Z |
| 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 | MyqZj0sJWw6Q |
| 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 |
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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 Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination Enterprise Document Intelligence [Vol.1 #8A] – The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable Kezhan Shi Jul 4, 2026 31 min read Share Photo by Anna Tarazevich, via Pexels. This article opens the generation brick of Enterprise Document Intelligence, a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, and generation. Generation is the fourth and last brick. This is the first of its three parts: the contract, the typed answer schema the model has to fill.
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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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