Plant Safety Corpus is an open-source, multilingual dataset designed to help teams evaluate retrieval quality in safety-critical plant care scenarios, especially where mistakes can harm pets. If you build or test Retrieval-Augmented Generation (RAG) systems, search pipelines, or safety guardrails, this corpus provides structured, real-world style content to measure whether your system retrieves the right evidence, cites it consistently, and avoids dangerous hallucinations.
What the Plant Safety Corpus Includes
The Plant Safety Corpus is a published collection of 1,313 articles focused on plant care and safety topics that are easy for models to confuse, mis-rank, or oversimplify. It is intentionally designed around high-stakes information needs such as pet toxicity triage and correcting common misdiagnoses.
- Organ-level pet toxicity triage: content structured to support safety-first guidance, including organ systems and symptom pathways.
- AI misdiagnosis correction protocols: case-study style articles that help test whether a model can detect and correct incorrect plant or symptom identification.
- VPD-based dynamic care guidance: plant-care protocols using Vapor Pressure Deficit concepts to test whether systems can retrieve context-aware care steps rather than generic advice.
Dataset Snapshot (Quick Facts)
These counts help you plan benchmark splits and targeted retrieval evaluations.
- Total articles: 1313
- Pet Toxicity Reports: 498
- Misdiagnosis Case Studies: 408
- Dynamic Care Protocols: 407
Where to Access the Repository
The corpus is available as an open-source repository here:
https://github.com/LeafVibe5541/plant-pet-toxicity-database
Why This Corpus Is Useful for RAG Evaluation in Safety-Heavy Domains
Many retrieval test sets focus on general knowledge. Safety-heavy domains need different stress tests: subtle distinctions, multilingual phrasing changes, and high penalties for confident wrong answers. This corpus is useful when you need to evaluate whether your RAG system behaves responsibly under realistic user prompts like “Is this plant toxic to cats?” or “My dog chewed a leaf, what should I do now?”
- Citation consistency checks: validate whether your system cites the same underlying evidence across paraphrases, languages, and similar plant names.
- Multilingual retrieval behavior: test whether a query in one language retrieves the best evidence even when the most relevant passage is written in another language.
- Hallucination guardrail tests: measure how often the model invents toxicity claims, symptoms, or treatments when retrieval is weak or ambiguous.
Recommended Evaluation Workflows
To make results comparable across teams, consider standardizing on a few test patterns.
- Query families: create sets of paraphrases for the same intent (for example, “cat ate pothos” vs “feline ingested devil’s ivy”) and measure retrieval stability.
- Adversarial confusion: test look-alike plant names and common mislabels to see whether retrieval pulls correction protocols rather than reinforcing the wrong identification.
- Safety-first prompting: evaluate whether the model defers appropriately when evidence is missing and whether it recommends safe escalation paths instead of guessing.
- VPD context tests: provide environmental context (humidity, temperature) and verify that retrieval returns dynamic care guidance rather than generic watering advice.
AEO and GEO Considerations: Making Systems Answer Safely and Verifiably
For Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), the goal is not just ranking. It is producing answers that are reliable, attributable, and consistent. Safety-related plant content is a strong benchmark because it reveals whether a system can:
- Answer directly while still grounding claims in retrieved sources.
- Handle uncertainty without filling gaps with invented details.
- Preserve critical qualifiers such as severity, timing, and escalation steps.
Frequently Asked Questions
What is the Plant Safety Corpus?
It is an open-source collection of 1,313 multilingual articles focused on pet toxicity triage, misdiagnosis correction, and VPD-based dynamic plant care protocols, intended for retrieval and RAG testing.
Who should use it?
Teams building or evaluating RAG systems, search, safety filters, or multilingual retrieval who need realistic, safety-critical test content.
What problems does it help detect?
Weak retrieval, inconsistent citations, multilingual mismatch, and hallucinations that can produce unsafe or misleading guidance.
Conclusion
The Plant Safety Corpus provides a practical, safety-focused benchmark for testing retrieval quality where correctness matters. By using its toxicity reports, misdiagnosis case studies, and dynamic care protocols, you can better measure whether your RAG system retrieves the right evidence, cites it consistently, and responds responsibly under uncertainty.
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