Whitepaper

Measuring and optimizing AI context

Learn about the Context Quality Score (CQS), a framework developed at Atlassian to measure the quality of the context AI uses. Explore its three drivers – completeness, connectedness, and relevance – and how they help you identify gaps and prioritize improvements.

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Executive summary

Your AI tools rely on the knowledge your organization captures. When that knowledge is missing, disconnected, or out of date, AI has to work harder to find what matters – and its answers can fall short.

This white paper introduces the Context Quality Score (CQS), a framework developed at Atlassian to measure the quality of the context AI uses. Explore its three drivers – completeness, connectedness, and relevance – and how they help you identify gaps and prioritize improvements.

See how Atlassian applies CQS to the Teamwork Graph, starting with the software development lifecycle. Learn how tracking context quality can guide practical actions, from keeping sources in sync and linking related work to archiving stale content.

Key takeaways

    Learn how the Context Quality Score measures completeness, connectedness, and relevance – and helps you identify gaps in the context AI uses.

    See how Atlassian applies the framework to Teamwork Graph, starting with the software development lifecycle.

    Explore practical ways to improve context quality, from keeping sources in sync and linking related work to archiving stale content.