How Weave is Replacing Story Points with LLMs and AI
Blog post from Weave
Weave introduces an innovative approach to measuring engineering team performance by leveraging large language models (LLMs) and domain-specific machine learning, offering a more objective and standardized alternative to traditional story points. Story points have been criticized for their subjectivity and inconsistency across teams, failing to capture the true quality and impact of work. In contrast, Weave evaluates pull requests and code reviews based on complexity, scope, and quality, providing real-time insights into team performance that are less susceptible to manipulation. By tracking actual output and quality rather than mere activity, Weave's analytics platform helps identify where teams allocate their time, offering comprehensive dashboards for easy review and analysis. It integrates with popular tools like GitHub and Jira, allowing seamless incorporation into existing workflows, and is particularly beneficial for distributed teams or organizations with multiple squads. This shift to AI-driven analytics allows for more effective resource allocation, improved code quality, and enhanced team collaboration, marking a significant advancement in engineering management practices.
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