开发评估基准
创建自定义数据集和基准，以系统地测试和比较不同的 RAG 配置及改进方案。
开发评估基准 是 CoddyKit 上的免费 LLM Apps in Production (RAG + Vector DB + Caching) 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 LLM Apps in Production (RAG + Vector DB + Caching) 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 LLM Apps in Production (RAG + Vector DB + Caching) 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Why RAG Benchmarks Matter
Welcome! In this lesson, we'll learn how to create custom evaluation benchmarks for your RAG systems. Benchmarks are like custom test sets that help you measure how well your RAG application performs.
They are crucial for understanding improvements, regressions, and ensuring your RAG system delivers accurate and relevant information to your users.
Custom Benchmarks: The Why
While public datasets like SQuAD or HotpotQA are great for general LLM evaluation, they often don't reflect your specific use case or domain.
- Domain Specificity: Your RAG needs to answer questions about your data.
- Nuance & Complexity: Public datasets might not capture the unique challenges your users face.
- Continuous Improvement: Custom benchmarks allow you to track performance against your evolving needs.
What Makes a RAG Benchmark?
A robust RAG evaluation benchmark typically consists of a few key parts:
- Query Set: A collection of representative questions or prompts.
- Ground Truth: The "correct" answers or relevant documents for each query.
- Evaluation Metrics: The criteria you'll use to measure performance (e.g., accuracy, relevance).
We'll focus on the first two components in this lesson.
Building a Great Query Set
Your query set should mirror the types of questions real users will ask. Think about:
- Real User Data: Analyze actual user queries or common support tickets.
- Diverse Topics: Cover a wide range of subjects relevant to your RAG's knowledge base.
- Varying Difficulty: Include simple, complex, and even ambiguous questions.
- Edge Cases: Don't forget queries that might challenge your system.
Example: Query Generation
You can start by manually crafting queries or by using an LLM to generate them based on your documents. Here's a simple Python example of a query set structure:
queries = [
"What are the benefits of cloud computing?",
"Explain the capital gains tax in detail.",
"How do I reset my account password?",
"What is the company's policy on remote work?",
"List common cybersecurity threats."
]
for q in queries:
print(f"Query: {q}")Establishing Ground Truth
Ground truth is the gold standard against which your RAG's output is measured. For RAG, this often means identifying:
- Relevant Documents: Which specific documents should be retrieved for a given query?
- Correct Answers: What is the ideal answer based on those documents?
This step often requires human expertise to ensure accuracy.
Structuring Ground Truth
Ground truth can be stored in a structured way, linking queries to their expected relevant context and answers. This allows for automated evaluation.
ground_truth = {
"What are the benefits of cloud computing?": {
"relevant_docs": ["doc_cloud_intro.txt", "doc_cloud_benefits.pdf"],
"answer": "Scalability, cost savings, flexibility, and reliability."
},
"How do I reset my account password?": {
"relevant_docs": ["doc_password_reset_guide.html"],
"answer": "Go to settings, click 'Forgot Password', and follow the prompts."
}
}
for query, gt in ground_truth.items():
print(f"Query: {query}")
print(f" Expected Docs: {gt['relevant_docs']}")
print(f" Expected Answer: {gt['answer']}\n")The Human Touch: Annotation
Creating high-quality ground truth often involves human annotation. This means:
- Experts Review: Subject matter experts identify relevant documents and craft ideal answers.
- Crowdsourcing: For larger datasets, platforms can be used, but quality control is vital.
- Consistency: Clear guidelines are essential to ensure annotators label data uniformly.
This ensures your benchmark accurately reflects "correctness."
Benchmarks Evolve
Your RAG system and its data will change over time, and so should your benchmarks! Treat your evaluation benchmarks as living assets:
- Add New Queries: Incorporate new user questions or emerging topics.
- Update Ground Truth: As your knowledge base grows, update expected answers.
- Retire Old Data: Remove outdated information that is no longer relevant.
Regular review keeps your benchmark effective.
Benchmark Essentials
Which of the following are essential components of a robust RAG evaluation benchmark?
Recap: Building Benchmarks
Great job! You've learned how to develop custom evaluation benchmarks for your RAG system. We covered:
- The importance of custom, domain-specific benchmarks.
- The core components: query sets and ground truth.
- Strategies for crafting representative queries and defining accurate ground truth.
- The role of human annotation and iterative refinement.
Next, we'll explore how to use these benchmarks to apply key metrics for RAG performance evaluation!
常见问题解答
「开发评估基准」课时是免费的吗？
是的 — 「开发评估基准」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 LLM Apps in Production (RAG + Vector DB + Caching) 课程的其余内容，请升级到 CoddyKit PRO。 LLM Apps in Production (RAG + Vector DB + Caching) 课程共包含 4 节课。
「开发评估基准」这节课中我会学到什么？
创建自定义数据集和基准，以系统地测试和比较不同的 RAG 配置及改进方案。 你通过在浏览器中直接运行的动手代码来练习 LLM Apps in Production (RAG + Vector DB + Caching)，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 LLM Apps in Production (RAG + Vector DB + Caching) 需要有经验吗？
无需任何先前经验。CoddyKit 上的 LLM Apps in Production (RAG + Vector DB + Caching) 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「开发评估基准」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 LLM Apps in Production (RAG + Vector DB + Caching) 课中编写并运行代码吗？
能。每节 LLM Apps in Production (RAG + Vector DB + Caching) 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。