[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Defi Rocketeer [@Defi_Rocketeer](/creator/twitter/Defi_Rocketeer) on x 171.4K followers Created: 2025-07-17 07:13:23 UTC How do we ensure that AI-generated data is not only accurate, but also trustworthy? I found an answer in @JoinSapien, as they’re building a clearly structured role-based contribution system. Instead of giving everyone the same permissions, they divide participants into three tiers: ▸ #Contributor - the first to create data, propose prompts, and evaluate AI responses. ▸ #Validator - those who review, challenge, and score based on community logic. ▸ #Enterprise Reviewer - domain experts from enterprises who handle high-stakes tasks (e.g., medical, legal, technical). I believe this shift matters for three key reasons: 1⃣ It increases transparency in evaluation and reduces one-sided bias. 2⃣ It allows incentives to match skill levels – no one is treated the same by default. 3⃣ It creates a clear, reliable pipeline for real-world AI model training. My take: any serious #AI system needs a serious review system behind it. #Sapien is laying that foundation.  XXXXX engagements  **Related Topics** [validator](/topic/validator) [coins ai](/topic/coins-ai) [Post Link](https://x.com/Defi_Rocketeer/status/1945743616581976259)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Defi Rocketeer @Defi_Rocketeer on x 171.4K followers
Created: 2025-07-17 07:13:23 UTC
How do we ensure that AI-generated data is not only accurate, but also trustworthy?
I found an answer in @JoinSapien, as they’re building a clearly structured role-based contribution system.
Instead of giving everyone the same permissions, they divide participants into three tiers:
▸ #Contributor - the first to create data, propose prompts, and evaluate AI responses.
▸ #Validator - those who review, challenge, and score based on community logic.
▸ #Enterprise Reviewer - domain experts from enterprises who handle high-stakes tasks (e.g., medical, legal, technical).
I believe this shift matters for three key reasons:
1⃣ It increases transparency in evaluation and reduces one-sided bias.
2⃣ It allows incentives to match skill levels – no one is treated the same by default.
3⃣ It creates a clear, reliable pipeline for real-world AI model training.
My take: any serious #AI system needs a serious review system behind it.
#Sapien is laying that foundation.
XXXXX engagements
