Infuse knowledge into language models with just 10 samples
Developers struggle to efficiently add domain-specific knowledge to large language models beyond context window limits without costly, slow iteration.
A fine-tuning approach that infuses knowledge into LLMs using as few as 10 samples, enabling rapid, cheap iteration on custom models.
Developers and ML teams building custom language models and AI applications.
Often running away from robots (that I created). Building the next generation of artifical narrow intelligence.
When not working on Automorphic, I'm building AI to beat me at chess
Building AI agents that can remember and self-improve like we can at Automorphic.




