Dosu achieved a 30% accuracy improvement with no prompt engineering by using LangSmith for continual in-context learning. Prompt engineering and fine-tuned models have drawbacks that Dosu avoids. In-context learning with optimal examples collected from users is simple and effective for Dosu's adaptive learning.
Table of contents
Continual In-Context Learning is Simple and EffectiveImplementing Continual Learning with LangSmithBuilding the World’s Best GitHub Auto LabelerContinual Learning is the Future of Agents4 Impressions