Pillar page · evaluation
LLM evaluation scorecards that teams can actually use
Move from vibe checks to observable criteria: reasoning, prompt design, evaluation discipline, and what “good” looks like in production—not just demo polish.
Signal over storytelling
LLM roles need explicit dimensions—latency, safety, eval harnesses, human-in-the-loop—otherwise panels overweight charisma. Scorecards keep debriefs honest.
Frameworks & rubrics
Start with these long-form pieces, then adapt weights to your org.
Blog
Evaluating ML and LLM candidates: a practical framework
A structured framework for technical screens and hiring-manager interviews—covering measurement discipline, system design, safety, and collaboration when you hire machine learning and large language model practitioners.
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Resource
A lightweight prompt-engineering interview rubric
A concise checklist for structured interviews when prompt design is part of the role.
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Resource
Portfolio signals for LLM and agent roles
What hiring teams look for in public profiles when evaluating LLM, RAG, and agentic systems experience.
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See how candidates present proof
Profiles foreground projects and tools; job posts mirror that vocabulary so you hire against the same bar you interview on.
Evaluation tips & hiring updates
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