How Dosu Used LangSmith to Achieve a 30% Accuracy Improvement with No Prompt Engineering

Devin Stein, CEO of Dosu, discusses how the company enhances its application performance using LangSmith without relying on prompt engineering. Instead, Dosu employs a continual in-context learning approach, where feedback from users is transformed into few-shot examples and integrated back into the application to adapt to organizational changes over time. Dosu acts as an engineering teammate, automating tasks like labeling, which is crucial for maintaining efficient workflows. Unlike prompt engineering and fine-tuning, which are limited by their static nature and complexity, continual in-context learning allows Dosu to dynamically learn from user corrections, improving its functionality and accuracy, as demonstrated by a significant increase in auto-labeling precision. This approach not only boosts performance but also empowers users to customize Dosu to their specific needs, creating a more interactive and adaptive product experience.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.