August 2026
Intermediate to advanced
552 pages
8h 9m
Japanese
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19章Gen AI Evaluation ServiceによるLLMとAIエージェントの品質評価
19.1 はじめに
本章の内容を学習することで、LLMやエージェントの出力品質を定量的かつ再現可能な方法で評価する手法を習得できます。これにより、主観的な判断に頼ることなく、モデルやプロンプトの改善サイクルを客観的に回せるようになります。さらに、エージェントではツール呼び出しの推論過程も含めた多角的な品質検証が可能となり、本番運用に耐えうる、信頼性の高いAIアプリケーションの構築を支える評価基盤を構築できます。
- この章の内容
- LLM-as-a-Judgeの概念と評価基準の設計
- Gen AI Evaluation Service（旧称：Gen AI evaluation service on Vertex AI）の全体像とGenAI Client API
- 組み込み評価指標（適応型ルーブリック、静的ルーブリック、計算ベースのメトリクス）の活用
- カスタムプロンプトとカスタム関数による独自評価指標の導入
- 単一LLM出力の評価と複数モデルの比較
- バッチ評価によるスケーラブルな品質測定
- ADKエージェントのTrajectory評価とFinal Response評価
本章では、Agent PlatformのGen AI Evaluation Serviceを活用したLLM/エージェント出力の品質評価手法を解説します。LLMを活用したアプリケーションの品質を「なんとなく」の主観ではなく、定量的に測定、改善するための実践的な手法を身につけることが目的です。
本章の実装はJupyter Notebook形式で進めていきます。コードはColab Enterprise上で実行する想定ですが、ローカル環境やAgent ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.