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Kamya Shah

Kamya Shah

LLM-as-a-Judge in Agentic Applications: Ensuring Reliable and Efficient AI Evaluation

LLM-as-a-Judge in Agentic Applications: Ensuring Reliable and Efficient AI Evaluation

TLDR LLM-as-a-Judge is an automated evaluation technique that uses large language models to assess and score the outputs of other models. This scalable approach enables nuanced, and rapid evaluations, outperforming traditional metrics and manual review in both speed and depth with scale by reading, reasoning about, and justifying scores across
Kamya Shah Sep 20, 2025
Session‑Level Observability: Tracking Multi‑Turn Conversations at Scale

Session‑Level Observability: Tracking Multi‑Turn Conversations at Scale

TL;DR Session-level observability is essential for tracking multi-turn conversations in modern AI applications. By monitoring interactions at the session level, teams can pinpoint issues, improve agent reliability, and ensure high-quality user experiences. Maxim AI offers comprehensive tools for session-level observability, enabling technical teams to monitor, evaluate, and optimize multi-turn
Kamya Shah Sep 18, 2025

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