AI startup Probably raises $9 million to combat model errors
Curated by the Inblix editorial team
TechCrunch AI
Probably aims to prevent hallucinations and factual errors in LLMs by building a rigorous error-catch system, utilizing a deterministic validator to refine context and reduce ambiguity. This approach enables smaller AI models, reducing costs and token usage, and can be applied to various precision-sensitive use cases.
💡 Key Takeaways
- Probably is trying to build a more rigorous way to catch errors in LLMs
- The company's goal is to prevent hallucinations and simple factual errors from reaching users
- Probably's data science tool uses a deterministic validator to refine context and reduce ambiguity
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