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An engineering manager shares how they use LLMs to automate the data-collection layer of performance reviews, while keeping all judgment, ratings, and feedback decisions firmly human. The workflow involves feeding context (competency matrix, rating rubric, review template) and MCP-connected tools (Linear, GitLab, Slack, Notion, Honeycomb) into a model to produce an evidence-backed research brief. The post draws a clear three-layer distinction: data collection (LLM-suitable), analysis (LLM-helpful), and judgment (human-only). It also addresses privacy boundaries, transparency with direct reports, and the ethical asymmetry of managers using AI to evaluate employees.

Human judgment, AI assistance

We spent the last decade managing teams, delivering on projects, and leading people. We learned our craft by combining data-backed research and our trials and errors.