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We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the same multi-turn shopping assistant agent.
I tested Jev and five LLM judges across ten Arize Phoenix evaluators to measure how often their answers changed alongside accuracy, cost, and latency. The post What Jev’s probabilities reveal that repeated LLM judgments miss appeared first on Arize AI.
We replaced the 58 tools PXI used to drive the Phoenix UI with two tools and a JavaScript sandbox that runs in your browser tab. Here's why, how it works, and what it cost. The post How we built UI Code Mode into Arize Phoenix appeared first on Arize AI.
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