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windyboy 0dac58fb6f reconcile LLM_Evaluation zone: align stage numbering and scopes, standardize full-path wikilinks, slim old guidebook notes, dedupe templates
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---
type: reference
tags:
- llm-evaluation
- evaluation-infrastructure
status: active
created: 2026-08-21
---
# 01 · Evaluation Infrastructure
主题:Evaluation Harness、Sandbox、Trace、Scaling
## S 级资源(必须认真研究)
### 1. UK AISI Engineering Playbook + Inspect AI
- 仓库:https://github.com/UKGovernmentBEIS/inspect_ai
- 背景:英国人工智能安全研究所(UK AISI)官方开源
- 为什么看:国家级安全评测机构测试前沿模型时使用的完整底座
- 核心思想:把评测基础设施拆成五层
- Evaluate
- Isolate
- Connect
- Run
- Scale
- 重点抽象(映射到自己的体系):
- Task → Case
- Dataset → Dataset
- Solver → Model / Agent Adapter
- Tool / Sandbox → 隔离执行环境
- Scorer → Grader
- Log → Trace / Outcome
- 适合阶段:完成第一个小项目以后
### 2. AWS Generative AI Evaluations Workshop
- 为什么看:目前垂直场景最全、最硬核的可运行实战代码
- 覆盖场景:Multimodal RAG / Tool Calling5 种渐进式评测方法)/ Automated ReasoningSMT 求解器)/ Multi-Agent Shared Context / Red Teaming(完整介绍与链接见 [[01_Projects/Personal-Tech/LLM_Evaluation/04-Reference/archive/01-Curated-External-Resources|archive/01]]
- 学习方式(重要):
不要只照着 Notebook 跑。每个模块都问:
- Task 是什么?
- Case 怎么构造?
- Rubric 是什么?
- Grader 是什么?
- Failure 如何定义?
- 如何做成 Regression
- 适合阶段:完成第一个小项目以后(与本节开头一致);精读顺序上建议作为四个 S 级资源中第一个上手(见 [[01_Projects/Personal-Tech/LLM_Evaluation/04-Reference/README|04-Reference 资源地图]]
## 次级参考
- Hugging Face evaluation-guidebook(已在本目录下:[[01_Projects/Personal-Tech/LLM_Evaluation/04-Reference/evaluation-guidebook/00-Overview|evaluation-guidebook]]
- DeepEval(应用级单元测试框架,上手快但抽象层较浅)
> 🔗 本页资源的完整链接、来源背景与上手建议见 [[01_Projects/Personal-Tech/LLM_Evaluation/04-Reference/archive/01-Curated-External-Resources|archive/01-Curated-External-Resources]]"国家级与顶级学术机构"与"云厂商生产环境"两节)。