你的RAG系统到底好不好？4个指标说清楚
你有没有过这种经历：改了切分策略，感觉检索效果好了一点但说不上来好多少；换了个Embedding模型，有些问题答得更好了但有些变差了；上线之后用户反馈"感觉不如以前了"，但你看代码什么都没改。
这就是没有评估体系的典型症状——优化全凭感觉，改了不知道好了没有，出了问题不知道哪里的问题。
RAG评估的本质就是给系统做"CT检查"——不是看整体"感觉好不好"，而是逐环节扫描，精确到"检索的召回率是多少"“生成的忠实度是多少"“哪个环节拉了后腿”。有了这些数据，优化才有方向。
为什么需要专门的RAG评估？
传统的NLP评估指标（BLEU、ROUGE）不适合RAG，因为RAG不是简单的文本生成，而是检索+增强+生成的复合系统。你需要知道的不只是"答案好不好”，还有：
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检索到的文档跟问题相关吗？（上下文精确度）
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检索到的文档全吗？有没有漏掉关键信息？（上下文召回率）
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模型的回答忠于检索到的文档吗？有没有编造？（忠实度）
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回答直接解决了用户的问题吗？（回答相关性）
这四个指标分别对应RAG的不同环节，哪个环节出问题就优化哪个。
RAGAS：RAG评估的标准工具
RAGAS（RAG Assessment）是目前最主流的RAG评估框架，开源免费，提供标准化的评估指标。
四个核心指标
1. Context Precision（上下文精确度）
检索到的文档中，有多少是真正相关的？
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高分：检索到5个文档，5个都跟问题相关
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低分：检索到5个文档，只有1个相关，其余4个是噪音
2. Context Recall（上下文召回率）
回答问题所需的信息，检索到了吗？
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高分：标准答案中的所有关键信息都能在检索结果中找到
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低分：标准答案中的重要信息检索结果里没有
3. Faithfulness（忠实度）
模型的回答是否忠实于检索到的文档？
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高分：回答中的每个论断都能在检索结果中找到依据
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低分：模型编造了检索结果中没有的信息
4. Answer Relevancy（回答相关性）
回答是否直接解决了用户的问题？
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高分：回答紧扣问题，不跑题
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低分：答非所问，或回答了很多无关内容
评估数据集
评估需要四个字段的数据：
<span leaf="">eval_data = {</span><br><span leaf=""> </span><span><span leaf="">"question"</span></span><span leaf="">: [</span><br><span leaf=""> </span><span><span leaf="">"公司年假政策是什么？"</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf="">"如何申请差旅报销？"</span></span><span leaf="">,</span><br><span leaf=""> ],</span><br><span leaf=""> </span><span><span leaf="">"contexts"</span></span><span leaf="">: [ </span><span><span leaf=""># 检索到的文档（自动获取或手动标注）</span></span><br><span leaf=""> [</span><span><span leaf="">"公司年假政策：入职满1年可享5天带薪年假..."</span></span><span leaf="">, </span><span><span leaf="">"年假申请需提前3天在OA系统提交..."</span></span><span leaf="">],</span><br><span leaf=""> [</span><span><span leaf="">"差旅报销流程：1. 填写报销单 2. 附上发票 3. 提交审批..."</span></span><span leaf="">],</span><br><span leaf=""> ],</span><br><span leaf=""> </span><span><span leaf="">"answer"</span></span><span leaf="">: [ </span><span><span leaf=""># RAG系统的实际回答</span></span><br><span leaf=""> </span><span><span leaf="">"入职满1年可享5天带薪年假，需提前3天申请。"</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf="">"填写报销单并附上发票后提交审批。"</span></span><span leaf="">,</span><br><span leaf=""> ],</span><br><span leaf=""> </span><span><span leaf="">"ground_truth"</span></span><span leaf="">: [ </span><span><span leaf=""># 标准答案（人工标注）</span></span><br><span leaf=""> </span><span><span leaf="">"入职满1年可享5天带薪年假，需提前3天在OA系统提交申请。"</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf="">"填写差旅报销单，附上原始发票，提交至直属上级审批。"</span></span><span leaf="">,</span><br><span leaf=""> ],</span><br><span leaf="">}</span><br>
运行评估
<span><span leaf="">from</span></span><span leaf=""> ragas </span><span><span leaf="">import</span></span><span leaf=""> evaluate</span><br><span><span leaf="">from</span></span><span leaf=""> ragas.metrics </span><span><span leaf="">import</span></span><span leaf=""> (</span><br><span leaf=""> context_precision,</span><br><span leaf=""> context_recall,</span><br><span leaf=""> faithfulness,</span><br><span leaf=""> answer_relevancy,</span><br><span leaf="">)</span><br><span><span leaf="">from</span></span><span leaf=""> datasets </span><span><span leaf="">import</span></span><span leaf=""> Dataset</span><br><br><span><span leaf=""># 构建评估数据集</span></span><br><span leaf="">eval_dataset = Dataset.from_dict(eval_data)</span><br><br><span><span leaf=""># 运行评估</span></span><br><span leaf="">result = evaluate(</span><br><span leaf=""> eval_dataset,</span><br><span leaf=""> metrics=[context_precision, context_recall, faithfulness, answer_relevancy],</span><br><span leaf="">)</span><br><br><span leaf="">print(result)</span><br><span><span leaf=""># 输出示例：</span></span><br><span><span leaf=""># {</span></span><br><span><span leaf=""># 'context_precision': 0.78,</span></span><br><span><span leaf=""># 'context_recall': 0.65,</span></span><br><span><span leaf=""># 'faithfulness': 0.85,</span></span><br><span><span leaf=""># 'answer_relevancy': 0.72,</span></span><br><span><span leaf=""># }</span></span><br>
解读评估结果
| 指标低
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说明
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优化方向
Context Precision低
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检索噪音多
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加Rerank、调相似度阈值、优化chunk
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Context Recall低
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检索遗漏多
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换Embedding、加混合检索、查询改写
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Faithfulness低
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模型在编
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改提示词、换模型、加来源标注要求
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Answer Relevancy低
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答非所问
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改查询理解、提示词加约束
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构建黄金测试集：评估的地基
评估结果的可靠性取决于测试集的质量。随便找几个问题测一下，结论不靠谱。
测试集构建方法
方法1：人工标注（最可靠）
从真实用户日志中抽取50-100个问题，人工标注标准答案：
<span><span leaf=""># 从日志中抽取真实问题</span></span><br><span leaf="">real_queries = [</span><br><span leaf=""> </span><span><span leaf="">"年假可以跨年累积吗？"</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf="">"出差补贴标准是多少？"</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf="">"试用期多长时间？"</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf=""># ...50-100个</span></span><br><span leaf="">]</span><br><br><span><span leaf=""># 人工标注标准答案</span></span><br><span leaf="">ground_truths = [</span><br><span leaf=""> </span><span><span leaf="">"年假不可跨年累积，当年未使用的年假在12月31日自动清零。"</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf="">"出差补贴分为住宿、交通、餐饮三项..."</span></span><span leaf="">,</span><br><span leaf=""> </span><span><span leaf="">"试用期根据合同期限不同..."</span></span><span leaf="">,</span><br><span leaf="">]</span><br>
方法2：LLM生成（快速但有偏差）
用模型基于文档生成问答对：
<span><span leaf="">from</span></span><span leaf=""> ragas.testset.generator </span><span><span leaf="">import</span></span><span leaf=""> TestsetGenerator</span><br><span><span leaf="">from</span></span><span leaf=""> ragas.testset.evolutions </span><span><span leaf="">import</span></span><span leaf=""> simple, reasoning, multi_context</span><br><br><span leaf="">generator = TestsetGenerator.from_langchain(model)</span><br><br><span><span leaf=""># 基于文档自动生成测试集</span></span><br><span leaf="">testset = generator.generate_with_langchain_docs(</span><br><span leaf=""> documents=docs,</span><br><span leaf=""> test_size=</span><span><span leaf="">50</span></span><span leaf="">, </span><span><span leaf=""># 生成50个测试用例</span></span><br><span leaf=""> distributions={simple: </span><span><span leaf="">0.5</span></span><span leaf="">, reasoning: </span><span><span leaf="">0.3</span></span><span leaf="">, multi_context: </span><span><span leaf="">0.2</span></span><span leaf="">},</span><br><span leaf=""> </span><span><span leaf=""># 50%简单问题 + 30%推理问题 + 20%多文档问题</span></span><br><span leaf="">)</span><br>
A/B测试：量化对比不同配置
改了配置到底好了还是差了？不能凭感觉，要A/B测试：
<span><span><span leaf="">def</span></span><span leaf=""> </span><span><span leaf="">run_ab_test</span></span><span><span leaf="">(config_a_name, config_a_retriever, </span><br><span leaf=""> config_b_name, config_b_retriever, </span><br><span leaf=""> test_dataset)</span></span><span leaf="">:</span></span><br><span leaf=""> </span><span><span leaf="">"""A/B测试：对比两种检索配置的RAGAS分数"""</span></span><br><span leaf=""> </span><br><span leaf=""> </span><span><span><span leaf="">def</span></span><span leaf=""> </span><span><span leaf="">run_rag</span></span><span><span leaf="">(retriever, question)</span></span><span leaf="">:</span></span><br><span leaf=""> docs = retriever.invoke(question)</span><br><span leaf=""> context = </span><span><span leaf="">"\n"</span></span><span leaf="">.join([d.page_content </span><span><span leaf="">for</span></span><span leaf=""> d </span><span><span leaf="">in</span></span><span leaf=""> docs])</span><br><span leaf=""> prompt = </span><span><span leaf="">f"基于以下内容回答问题：\n</span><span><span leaf="">{context}</span></span><span leaf="">\n\n问题：</span><span><span leaf="">{question}</span></span><span leaf="">"</span></span><br><span leaf=""> </span><span><span leaf="">return</span></span><span leaf=""> model.invoke(prompt).content, [d.page_content </span><span><span leaf="">for</span></span><span leaf=""> d </span><span><span leaf="">in</span></span><span leaf=""> docs]</span><br><span leaf=""> </span><br><span leaf=""> results_a = {</span><span><span leaf="">"question"</span></span><span leaf="">: [], </span><span><span leaf="">"contexts"</span></span><span leaf="">: [], </span><span><span leaf="">"answer"</span></span><span leaf="">: [], </span><span><span leaf="">"ground_truth"</span></span><span leaf="">: []}</span><br><span leaf=""> results_b = {</span><span><span leaf="">"question"</span></span><span leaf="">: [], </span><span><span leaf="">"contexts"</span></span><span leaf="">: [], </span><span><span leaf="">"answer"</span></span><span leaf="">: [], </span><span><span leaf="">"ground_truth"</span></span><span leaf="">: []}</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf="">for</span></span><span leaf=""> item </span><span><span leaf="">in</span></span><span leaf=""> test_dataset:</span><br><span leaf=""> </span><span><span leaf=""># Config A</span></span><br><span leaf=""> answer_a, contexts_a = run_rag(config_a_retriever, item[</span><span><span leaf="">"question"</span></span><span leaf="">])</span><br><span leaf=""> results_a[</span><span><span leaf="">"question"</span></span><span leaf="">].append(item[</span><span><span leaf="">"question"</span></span><span leaf="">])</span><br><span leaf=""> results_a[</span><span><span leaf="">"contexts"</span></span><span leaf="">].append(contexts_a)</span><br><span leaf=""> results_a[</span><span><span leaf="">"answer"</span></span><span leaf="">].append(answer_a)</span><br><span leaf=""> results_a[</span><span><span leaf="">"ground_truth"</span></span><span leaf="">].append(item[</span><span><span leaf="">"ground_truth"</span></span><span leaf="">])</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># Config B</span></span><br><span leaf=""> answer_b, contexts_b = run_rag(config_b_retriever, item[</span><span><span leaf="">"question"</span></span><span leaf="">])</span><br><span leaf=""> results_b[</span><span><span leaf="">"question"</span></span><span leaf="">].append(item[</span><span><span leaf="">"question"</span></span><span leaf="">])</span><br><span leaf=""> results_b[</span><span><span leaf="">"contexts"</span></span><span leaf="">].append(contexts_b)</span><br><span leaf=""> results_b[</span><span><span leaf="">"answer"</span></span><span leaf="">].append(answer_b)</span><br><span leaf=""> results_b[</span><span><span leaf="">"ground_truth"</span></span><span leaf="">].append(item[</span><span><span leaf="">"ground_truth"</span></span><span leaf="">])</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># 评估</span></span><br><span leaf=""> score_a = evaluate(Dataset.from_dict(results_a), metrics=[...])</span><br><span leaf=""> score_b = evaluate(Dataset.from_dict(results_b), metrics=[...])</span><br><span leaf=""> </span><br><span leaf=""> print(</span><span><span leaf="">f"Config A (</span><span><span leaf="">{config_a_name}</span></span><span leaf="">): </span><span><span leaf="">{score_a}</span></span><span leaf="">"</span></span><span leaf="">)</span><br><span leaf=""> print(</span><span><span leaf="">f"Config B (</span><span><span leaf="">{config_b_name}</span></span><span leaf="">): </span><span><span leaf="">{score_b}</span></span><span leaf="">"</span></span><span leaf="">)</span><br>
实测案例：对比纯向量检索 vs 混合检索+Rerank
| 指标
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纯向量
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混合+Rerank
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提升
Context Precision
|
0.72
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0.88
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+22%
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Context Recall
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0.68
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0.82
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+21%
| |
Faithfulness
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0.83
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0.86
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+4%
| |
Answer Relevancy
|
0.71
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0.79
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+11%
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Bad Case分析：定位"谁拖了后腿"
整体分数只能告诉你"好不好"，Bad Case分析能告诉你"为什么不好"。
<span><span><span leaf="">def</span></span><span leaf=""> </span><span><span leaf="">bad_case_analysis</span></span><span><span leaf="">(eval_results, test_dataset, threshold=</span><span><span leaf="">0.5</span></span><span leaf="">)</span></span><span leaf="">:</span></span><br><span leaf=""> </span><span><span leaf="">"""分析得分低的测试用例，定位瓶颈"""</span></span><br><span leaf=""> bad_cases = []</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf="">for</span></span><span leaf=""> i, scores </span><span><span leaf="">in</span></span><span leaf=""> enumerate(eval_results):</span><br><span leaf=""> </span><span><span leaf="">if</span></span><span leaf=""> any(v < threshold </span><span><span leaf="">for</span></span><span leaf=""> v </span><span><span leaf="">in</span></span><span leaf=""> scores.values()):</span><br><span leaf=""> case = {</span><br><span leaf=""> </span><span><span leaf="">"question"</span></span><span leaf="">: test_dataset[i][</span><span><span leaf="">"question"</span></span><span leaf="">],</span><br><span leaf=""> </span><span><span leaf="">"answer"</span></span><span leaf="">: test_dataset[i][</span><span><span leaf="">"answer"</span></span><span leaf="">],</span><br><span leaf=""> </span><span><span leaf="">"scores"</span></span><span leaf="">: scores,</span><br><span leaf=""> </span><span><span leaf="">"diagnosis"</span></span><span leaf="">: </span><span><span leaf="">""</span></span><span leaf="">,</span><br><span leaf=""> }</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># 诊断</span></span><br><span leaf=""> </span><span><span leaf="">if</span></span><span leaf=""> scores[</span><span><span leaf="">"context_precision"</span></span><span leaf="">] < threshold:</span><br><span leaf=""> case[</span><span><span leaf="">"diagnosis"</span></span><span leaf="">] += </span><span><span leaf="">"🔍 检索噪音多（Context Precision低）→ 优化检索/Rerank "</span></span><br><span leaf=""> </span><span><span leaf="">if</span></span><span leaf=""> scores[</span><span><span leaf="">"context_recall"</span></span><span leaf="">] < threshold:</span><br><span leaf=""> case[</span><span><span leaf="">"diagnosis"</span></span><span leaf="">] += </span><span><span leaf="">"📭 检索遗漏（Context Recall低）→ 优化Embedding/查询改写 "</span></span><br><span leaf=""> </span><span><span leaf="">if</span></span><span leaf=""> scores[</span><span><span leaf="">"faithfulness"</span></span><span leaf="">] < threshold:</span><br><span leaf=""> case[</span><span><span leaf="">"diagnosis"</span></span><span leaf="">] += </span><span><span leaf="">"🤥 模型编造（Faithfulness低）→ 优化提示词/换模型 "</span></span><br><span leaf=""> </span><span><span leaf="">if</span></span><span leaf=""> scores[</span><span><span leaf="">"answer_relevancy"</span></span><span leaf="">] < threshold:</span><br><span leaf=""> case[</span><span><span leaf="">"diagnosis"</span></span><span leaf="">] += </span><span><span leaf="">"↗️ 答非所问（Answer Relevancy低）→ 优化查询理解 "</span></span><br><span leaf=""> </span><br><span leaf=""> bad_cases.append(case)</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf="">return</span></span><span leaf=""> bad_cases</span><br>
这是RAG调优最有效的方法：找到低分case→定位是哪个环节→针对性优化→重新评估→看分数有没有涨。
Java类比：这就像你用JUnit做单元测试——先跑一遍覆盖率报告，找到覆盖率最低的类，针对性补测试用例，再跑一遍看覆盖率涨了没。RAGAS评估就是RAG的JaCoCo+JUnit。
自动化评估流水线
把评估集成到CI/CD中，每次改代码自动跑评估：
<span><span leaf=""># eval_pipeline.py</span></span><br><span><span leaf="">import</span></span><span leaf=""> json</span><br><span><span leaf="">from</span></span><span leaf=""> ragas </span><span><span leaf="">import</span></span><span leaf=""> evaluate</span><br><span><span leaf="">from</span></span><span leaf=""> ragas.metrics </span><span><span leaf="">import</span></span><span leaf=""> context_precision, context_recall, faithfulness, answer_relevancy</span><br><br><span><span><span leaf="">def</span></span><span leaf=""> </span><span><span leaf="">run_evaluation</span></span><span><span leaf="">()</span></span><span leaf="">:</span></span><br><span leaf=""> </span><span><span leaf=""># 1. 加载测试集</span></span><br><span leaf=""> testset = json.load(open(</span><span><span leaf="">"golden_testset.json"</span></span><span leaf="">))</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># 2. 运行RAG获取回答</span></span><br><span leaf=""> results = run_rag_on_testset(testset)</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># 3. 运行RAGAS评估</span></span><br><span leaf=""> scores = evaluate(</span><br><span leaf=""> Dataset.from_dict(results),</span><br><span leaf=""> metrics=[context_precision, context_recall, faithfulness, answer_relevancy],</span><br><span leaf=""> )</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># 4. 与基线对比</span></span><br><span leaf=""> baseline = json.load(open(</span><span><span leaf="">"baseline_scores.json"</span></span><span leaf="">))</span><br><span leaf=""> </span><br><span leaf=""> print(</span><span><span leaf="">"=== RAG评估报告 ==="</span></span><span leaf="">)</span><br><span leaf=""> </span><span><span leaf="">for</span></span><span leaf=""> metric, score </span><span><span leaf="">in</span></span><span leaf=""> scores.items():</span><br><span leaf=""> diff = score - baseline.get(metric, </span><span><span leaf="">0</span></span><span leaf="">)</span><br><span leaf=""> arrow = </span><span><span leaf="">"↑"</span></span><span leaf=""> </span><span><span leaf="">if</span></span><span leaf=""> diff > </span><span><span leaf="">0</span></span><span leaf=""> </span><span><span leaf="">else</span></span><span leaf=""> </span><span><span leaf="">"↓"</span></span><br><span leaf=""> print(</span><span><span leaf="">f"</span><span><span leaf="">{metric}</span></span><span leaf="">: </span><span><span leaf="">{score:</span><span><span leaf="">.3</span></span><span leaf="">f}</span></span><span leaf=""> (</span><span><span leaf="">{arrow}</span></span><span><span leaf="">{abs(diff):</span><span><span leaf="">.3</span></span><span leaf="">f}</span></span><span leaf="">)"</span></span><span leaf="">)</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># 5. 分数低于阈值则告警</span></span><br><span leaf=""> </span><span><span leaf="">if</span></span><span leaf=""> scores[</span><span><span leaf="">"faithfulness"</span></span><span leaf="">] < </span><span><span leaf="">0.7</span></span><span leaf="">:</span><br><span leaf=""> print(</span><span><span leaf="">"⚠️ Faithfulness低于0.7，模型可能在编造答案！"</span></span><span leaf="">)</span><br><span leaf=""> </span><br><span leaf=""> </span><span><span leaf=""># 6. 保存新基线</span></span><br><span leaf=""> json.dump(dict(scores), open(</span><span><span leaf="">"baseline_scores.json"</span></span><span leaf="">, </span><span><span leaf="">"w"</span></span><span leaf="">))</span><br><br><span><span leaf="">if</span></span><span leaf=""> __name__ == </span><span><span leaf="">"__main__"</span></span><span leaf="">:</span><br><span leaf=""> run_evaluation()</span><br>
每次改了RAG配置后跑一遍这个脚本，5分钟就能知道改好了还是改坏了。
我的使用建议
-
先建测试集再优化。没有测试集的优化就是盲人摸象。50个测试用例就够起步了。
-
先跑基线分数。在优化之前先跑一遍评估，记录基线。后面所有改动都跟基线对比。
-
每次只改一个变量。同时改切分+Embedding+Rerank，你永远不知道哪个改动有效。
-
关注Bad Case多于平均分。平均分80%但有几个0分的Bad Case，可能比平均分75%但所有case都及格更危险。
-
Faithfulness是最重要的指标。模型编造答案比找不到答案更可怕——用户可能基于错误信息做决策。
本篇要点
| 要点
|
说明
四个核心指标
|
Context Precision/Recall + Faithfulness/Answer Relevancy
| |
RAGAS框架
|
开源RAG评估工具，自动化跑分
| |
黄金测试集
|
50-100个真实问题+人工标注答案
| |
A/B测试
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每次只改一个变量，对比前后分数
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Bad Case分析
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找低分case→定位瓶颈→针对性优化
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自动化流水线
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集成到CI/CD，改了就跑评估
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最重要指标
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Faithfulness > Recall > Precision > Relevancy
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RAG优化速查checklist
| 症状
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可能原因
|
优先尝试
Recall低
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chunk太大/Embedding差/Top-K太小
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调小chunk → 换BGE-m3 → 加BM25 → 调大K
| |
Precision低
|
没Rerank/Top-K太大/无过滤
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加Rerank → 降K → 加元数据过滤
| |
Faithfulness低
|
Prompt没约束/模型太弱/温度高
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加"仅基于上下文"约束 → temperature=0 → 换模型
| |
Relevancy低
|
查询理解差/检索到无关
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查询改写 → 检查切分 → 换Prompt模板
| |
改了没效果
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没有评估体系
|
先建50个测试用例，跑基线再调
|
下篇预告
最后一篇讲RAG的生产部署——增量索引策略、监控告警搭建、成本控制、多租户架构。把RAG从"能跑"变成"线上稳定运行"。
你有在做RAG评估吗？用什么工具？测试集怎么建的？评论区分享下经验。
觉得有用就点个在看，下一篇讲RAG生产部署——上线一周就翻车的事我见过太多了。
- 原文作者：知识铺
- 原文链接：https://index.zshipu.com/ai002/post/20260822/%E4%BD%A0%E7%9A%84RAG%E7%B3%BB%E7%BB%9F%E5%88%B0%E5%BA%95%E5%A5%BD%E4%B8%8D%E5%A5%BD4%E4%B8%AA%E6%8C%87%E6%A0%87%E8%AF%B4%E6%B8%85%E6%A5%9A/
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