Why can AI tools give wrong answers, known as "hallucinations"?
You may already have used AI tools such as ChatGPT, Gemini, Claude or others to look for information on cosmetic evaluation methods, providers or scientific references. These tools, called large language models (LLMs), are powerful, but they have a major flaw: they can give false, entirely made-up information, known as "hallucinations". The tool may apologize when you point it out, but the information remains unreliable.
What does this mean? In practice, an LLM does not really "know" things. It generates text that seems plausible, based on word sequences and text patterns. It works like a giant statistical prediction machine, predicting each time the most plausible next word in a sequence. Sometimes it invents facts, fabricates references or mixes up information while sounding perfectly confident. This is what is called a hallucination.
In everyday conversation, this is a minor problem. But in a scientific and regulatory context such as the evaluation of dermocosmetic ingredients and products, a hallucination can have serious consequences: wrong testing methods, non-existent providers or inaccurate claims can lead to costly mistakes and compliance risks.
This is precisely why having data checked and validated by people is essential.
What sets Skinobs apart from AIs and LLMs?
Skinobs was designed to be a reliable reference platform for the cosmetic evaluation industry. Here is how we guarantee the robustness of every piece of information you find on our platform:
🔒 Confidential, with controlled access
Every Skinobs user account is validated manually by our team. You will find neither bots nor fake profiles on the platform. This manual verification process ensures that only legitimate professionals of the cosmetics and evaluation industry can access the platform's content.
Since access is restricted to verified users, Skinobs data is neither indexed by search engines nor scraped by AI models. The information stays where it belongs: in the hands of qualified professionals.
✅ Information validated by people
Every evaluation method referenced on Skinobs is directly provided and validated by the laboratory that offers it, through a multi-step referencing process. This means:
No information scraped from the web by a robot
No unverified claims
Every method sheet is a real scientific reference, provided and validated by the laboratory, then kept up to date with them
When you find an evaluation laboratory on the Skinobs platform, you can be sure it exists. When you find an evaluation method on Skinobs, you can be certain it exists, because the laboratory or the device manufacturer sent us its exact characteristics.
🔐 A closed ecosystem for skin, hair and scalp testing professionals
Unlike open web sources accessible to everyone (including robots), Skinobs is a closed platform. You must be logged in to a validated account to browse methods, contact providers or access detailed information.
This closed approach protects:
Providers, whose detailed services are only visible to the professionals concerned,
Evaluation managers, who can make decisions based on verified data rather than approximations,
Data integrity, protected from any misuse or misinterpretation by AI crawlers.
In short
| Open web / AI tools | Skinobs |
Source of information | Scraped, aggregated, sometimes invented | Provided directly by laboratories |
Verification | None or automated | Manual validation by Skinobs + providers |
Access | Open to everyone, bots included | Reserved for verified professionals |
Risk of hallucination | High | No invented information: every method is a real reference |
Freshness of data | Depends on training data | Kept up to date with laboratories since 2016 |
Skinobs gives you the certainty that what you read is real and verified, because in science, there is no room for hallucinations.