windyboy
0dac58fb6f
- fix stage-numbering conflict (README vs 05-Progress) and unify stage-1 reading scope - resolve AWS workshop prerequisite contradiction in 04-Reference/01 - convert medium-path wikilinks to vault-root paths (~30 links), fix .pyy typos, annotate ragas fork, unify archive status, add 01-/02- README hubs - compress old-version guidebook notes (01, 05) into pointers; add 2026 reading guidance to 00-Overview - dedupe project templates and remove embedded template copy in 03-Practice/README
38 lines
1.2 KiB
Markdown
38 lines
1.2 KiB
Markdown
---
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type: reference
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tags:
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- llm-evaluation
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- continuous-evaluation
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- ci
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status: active
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created: 2026-08-21
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---
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# 03 · Continuous Evaluation
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主题：把评测从「手工跑一次」升级为「每次变更都会跑」
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## A 级资源
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### 1. CircleCI + RAGAS Continuous Eval
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- 价值不在 RAGAS 本身，而在：
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Eval → CI → Threshold → Gate
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- 真正要学习的是：
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Metric → Baseline → Threshold → Action
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例如：
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- security_failure > 0 → fail
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- overall_pass_rate ↓ → review
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- latency regression → review
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- RAGAS 只是 grader/metric 工具
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- 适合阶段：项目已有 regression set 后
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### 2. DeepLearning.AI + Weights & Biases
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- 真正适合学习的：
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Experiment Tracking / Run / Artifact / Version / Trace / Comparison
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- 建议用途：当你的目录开始出现
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runs/、runs-v2/、runs-final/、runs-final-new/
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说明该学 experiment tracking 了
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- 不建议：一开始就为了"专业"搭 W&B
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> 🔗 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]]（"云厂商生产环境"与"名校/大牛的工业级课程"两节）。
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