LangChain’s Align Evals closes the evaluator trust gap with prompt-level calibration
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now As enterprises increasingly turn to AI models to ensure their applications function well and are reliable, the gaps between model-led evaluations and human evaluations have only become clearer. To combat this, LangChain added Align Evals to LangSmith, a way to bridge the gap between large language model-based evaluators and human preferences and reduce noise. Align Evals enables LangSmith users to create their own LLM-based evaluators and calibrate them to align more closely with company preferences. “But, one big challenge we hear consistently from teams is: ‘Our evaluation scores don’t match what we’d expect a…
