Calibrating LLM-as-a-Judge: Why Evaluation Needs to Evolve
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Calibrating LLM-as-a-Judge: Why Evaluation Needs to Evolve
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As AI systems become more agentic and interconnected, evaluation is turning into one of the most important layers of the stack. At GoDaddy, we’ve been studying how LLMs behave when used as evaluators—not generators—and what it takes to trust their judgments.
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As AI systems become more agentic and interconnected, evaluation is turning into one of the most important layers of the stack. At GoDaddy, we’ve been studying how LLMs behave when used as evaluators—not generators—and what it takes to trust their judgments.
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A few highlights from our latest engineering write-up:
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A few highlights from our latest engineering write-up:
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🔹 Raw LLM scores drift and disagree, even on identical inputs
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🔹 Raw LLM scores drift and disagree, even on identical inputs
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🔹 Calibration curves help stabilize model scoring behavior
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🔹 Calibration curves help stabilize model scoring behavior
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🔹 Multi-model consensus reduces single-model bias and variance
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🔹 Multi-model consensus reduces single-model bias and variance
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🔹 These techniques support safer agent-to-agent decision making and strengthen our broader trust infrastructure (ANS, agentic systems, etc.)
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🔹 These techniques support safer agent-to-agent decision making and strengthen our broader trust infrastructure (ANS, agentic systems, etc.)
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If you're building agents, autonomous systems, or any pipeline that relies on “AI judging AI,” calibration isn’t optional — it's foundational.
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If you're building agents, autonomous systems, or any pipeline that relies on “AI judging AI,” calibration isn’t optional — it's foundational.
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👉 Full write-up: Calibrating Scores of LLM-as-a-Judge
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👉 Full write-up: Calibrating Scores of LLM-as-a-Judge
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https://www.godaddy.com/resources/news/calibrating-scores-of-llm-as-a-judge
https://www.godaddy.com/resources/news/calibrating-scores-of-llm-as-a-judge
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Would love feedback from the HF community:
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Would love feedback from the HF community:
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How are you calibrating or benchmarking model evaluators in your own workflows?
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How are you calibrating or benchmarking model evaluators in your own workflows?
Calibrating LLM-as-a-Judge: Why Evaluation Needs to Evolve

As AI systems become more agentic and interconnected, evaluation is turning into one of the most important layers of the stack. At GoDaddy, we’ve been studying how LLMs behave when used as evaluators—not generators—and what it takes to trust their judgments.

A few highlights from our latest engineering write-up:

🔹 Raw LLM scores drift and disagree, even on identical inputs
🔹 Calibration curves help stabilize model scoring behavior
🔹 Multi-model consensus reduces single-model bias and variance
🔹 These techniques support safer agent-to-agent decision making and strengthen our broader trust infrastructure (ANS, agentic systems, etc.)

If you're building agents, autonomous systems, or any pipeline that relies on “AI judging AI,” calibration isn’t optional — it's foundational.

👉 Full write-up: Calibrating Scores of LLM-as-a-Judge
https://www.godaddy.com/resources/news/calibrating-scores-of-llm-as-a-judge

Would love feedback from the HF community:
How are you calibrating or benchmarking model evaluators in your own workflows?
👀
John6666