You’re not alone - 75% of leaders think generative AI can reshape their industry, but only 6% have a strategy and plan to leverage it effectively.
Check out the white paper to see how top AI teams use human experts throughout the model development lifecycle to get the most out of generative AI in their organizations.
High-quality generative AI models are essential in industries where inaccuracies can have severe consequences. The paper highlights risks like inappropriate tone and fabricated information from AI outputs.
Discover why integrating human expertise at scale throughout the AI model development cycle is crucial. Human experts improve data quality, monitor outputs, and ensure models meet standards.
Learn strategies for engaging experts to fine-tune AI models, including reinforcement learning with expert feedback (RLEF). Understand how to aggregate expert opinions to achieve consensus and maintain quality.
Explore methods for accessing proprietary data to enhance AI models. The paper discusses why public datasets often fall short and how expert involvement addresses this gap.
See how Centaur.AI partnered with companies to improve model quality by increasing accuracy through expert-labeled data.
Learn how organizations can build high-quality AI systems by leveraging human expertise and unique datasets, ensuring reliable model performance and risk management.
This white paper offers actionable strategies to enhance AI quality through human involvement
“Working with Centaur.AI is the best way to get thousands of pieces of data annotated in a day, rather than weeks.
With Centaur.AI we have both an army of people doing high-quality annotations, and annotating the data very quickly. We were able to improve our model dramatically - from .6 to .83 F1 score - as a result.”
