LLM-as-a-Judge: How to Build an Automated Evaluation Pipeline You Can Trust | HackerNoon

Learn how to build an LLM-as-a-Judge pipeline with LangChain and Claude to score helpfulness and correctness at production scale.

칼 뉴포트가 분석한 AI 에이전트 2025년 약속이 빗나간 이유

<p>2025년 OpenAI 샘 알트먼 등은 AI 에이전트가 노동력에 합류해 생산성을 혁신할 것이라고 예측했으나, 실제 제품(예: ChatGPT Agent)은 단순 작...

Anthropic 엔지니어링: AI 에이전트 평가(Evals)의 실용적 가이드와 방법론

<p>요약:</p> <ul> <li>기존 LLM 벤치마크만으로는 도구 사용과 다단계 추론을 수행하는 'AI 에이전트'의 성능을 정확히 측정하기 어려움.</li> <l...

Task-free intelligence testing of LLMs (Part 1)

Evaluating Evaluation Metrics -- The Mirage of Hallucination Detection

Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and factuality concerns, the robustness and generalization of these metrics are still untested. In this paper, we conduct a large-scale empirical evaluation of 6 diverse sets of hallucination detection metrics across 4 datasets, 37 language models from 5 families, and 5 decoding methods. Our extensive investigation reveals concerning gaps in current hallucination evaluation: metrics often fail to align with human judgments, take an overtly myopic view of the problem, and show inconsistent gains with parameter scaling. Encouragingly, LLM-based evaluation, particularly with GPT-4, yields the best overall results, and mode-seeking decoding methods seem to reduce hallucinations, especially in knowledge-grounded settings. These findings underscore the need for more robust metrics to understand and quantify hallucinations, and better strategies to mitigate them.

Toward Holistic Evaluation of LLMs: Integrating Human Feedback with Traditional Metrics | HackerNoon

A deep dive into why LLMs need both metrics and human feedback for real-world accuracy, ethics, and performance.

How to evaluate an LLM system

Testing LLM applications need specialized evaluation techniques. Read how you can ensure they meet performance and reliability standards.