Handshake is the largest early-career network, specializing in connecting students and new grads with employers and career centers. It’s also an engineering powerhouse and innovator in applying AI to its... The post How Handshake Deployed and Scaled 15+ LLM Use Cases In Under Six Months — With Evals From Day One appeared first on Arize AI.| Arize AI
When LLMs are used as evaluators, two design choices often determine the quality and usefulness of their judgments: whether to require explanations for decisions, and whether to use explicit chain-of-thought... The post Evidence-Based Prompting Strategies for LLM-as-a-Judge: Explanations and Chain-of-Thought appeared first on Arize AI.| Arize AI
Most commonly, we hear about evaluating LLM applications at the span level. This involves checking whether a tool call succeeded, whether an LLM hallucinated, or whether a response matched expectations.... The post Trace-Level LLM Evaluations with Arize AX appeared first on Arize AI.| Arize AI
When evaluating AI applications, we often look at things like tool calls, parameters, or individual model responses. While this span-level evaluation is useful, it doesn’t always capture the bigger picture...| Arize AI
When To Build Custom Evaluators Arize-Phoenix ships with pre-built evaluators that are tested against benchmark datasets and tuned for repeatability. They’re a fast way to stand up rigorous evaluation for... The post LLM-as-a-Judge: Example of How To Build a Custom Evaluator Using a Benchmark Dataset appeared first on Arize AI.| Arize AI