The model observes, a table decides: keeping an LLM out of pet triage
TailStory, a pet health application, uses a vision model to describe physical symptoms while a static lookup table determines the medical urgency. This architectural choice ensures deterministic, reviewable, and testable triage outcomes that are not subject to the variability of large language models. The system employs specific rules and escalators to adjust urgency based on pet age and symptom combinations, preventing false reassurances or unnecessary alarms.
- ▪The vision model is restricted to reporting observable facts like color and consistency, while a TypeScript table maps these observations to urgency bands.
- ▪Escalators automatically increase the urgency level for very young or senior pets and for specific combinations of symptoms like watery stool with fresh blood.
- ▪The system includes a post-processing sweep to remove any medical claims or diagnoses from the model's summary text to ensure only factual observations are displayed.
- ▪A recent bug in the system incorrectly rated an obese cat as having an ideal body condition, highlighting the challenges of maintaining accuracy in automated health assessments.
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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 | TailStory |
| Canonical URL | https://tailstory-app.com/en/blog/model-observes-table-decides |
| Publication time | Thu, 24 Sep 2026 22:48:41 +0000 |
| Retrieval time | 2026-09-24T22:55:26.869Z |
| Last seen | 2026-09-24T22:55:26.869Z |
| 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 | EFR9wQ-tgmFC · 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
TailStoryBlogInside TailStoryInside TailStoryThe model observes, a table decides: keeping an LLM out of pet triageA vision model describes a pet's stool, ear or gum. Whether that means "see a vet now" is a row in a table the model never touches. Why, the rules, and the bug that rated an obese cat ideal.By TailStory team·25 September 2026·8 min readIn this articleWhy not let the model decideWhat the model is allowed to sayWhat the table decidesTrends: the field that keeps alerts believableThree things the spec got wrongThe bug that rated an obese cat as idealWhat this costs usPoint a vision model at a photo of a dog's gums and ask what's wrong, and it will tell you. It will sound exactly as sure when it's wrong, and it may give a different answer to the same photo tomorrow.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at TailStory.
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