Uploaded October 2025 | Updated September 2026, 2 weeks ago
When building and testing AI agents, one practical question that arises is whether to use the same model for both the agent’s reasoning and the evaluation of its outputs. Keeping the model consistent may simplify the setup and reduce costs, but it also raises concerns about bias, over-familiarity, and inflated scores. To better understand these trade-offs, we ran an experiment comparing how evaluations differ when the same model is used versus when evaluation is handled by a different model.This session covers the findings and implications.
More on LLM self-eval bias: arize.com/blog/should-i-use-the-same-llm-for-my-eval-as-my-agent-testing-self-evaluation-bias
When building and testing AI agents, one practical question that arises is whether to use the same model for both the agent’s reasoning and the evaluation of its outputs. Keeping the model consistent may simplify the setup and reduce costs, but it also raises concerns about bias, over-familiarity, and inflated scores. To better understand these trade-offs, we ran an experiment comparing how evaluations differ when the same model is used versus when evaluation is handled by a different model.This session covers the findings and implications.
More on LLM self-eval bias: arize.com/blog/should-i-use-the-same-llm-for-my-eval-as-my-agent-testing-self-evaluation-bias
