評価ベンチマークの開発
カスタムデータセットとベンチマークを作成し、さまざまなRAG構成や改善内容を体系的にテスト・比較します。
「評価ベンチマークの開発」はCoddyKit上の無料LLM Apps in Production (RAG + Vector DB + Caching)レッスンです。 これはレッスン2/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはLLM Apps in Production (RAG + Vector DB + Caching)学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 LLM Apps in Production (RAG + Vector DB + Caching)コースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
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!
よくある質問
「評価ベンチマークの開発」レッスンは無料ですか？
はい。「評価ベンチマークの開発」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、LLM Apps in Production (RAG + Vector DB + Caching)コースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 LLM Apps in Production (RAG + Vector DB + Caching)コースには全4レッスンが含まれています。
「評価ベンチマークの開発」で何を学びますか？
カスタムデータセットとベンチマークを作成し、さまざまなRAG構成や改善内容を体系的にテスト・比較します。 ブラウザで直接実行するハンズオンコードでLLM Apps in Production (RAG + Vector DB + Caching)を演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
LLM Apps in Production (RAG + Vector DB + Caching)を始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのLLM Apps in Production (RAG + Vector DB + Caching)は初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン2/4です。
「評価ベンチマークの開発」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このLLM Apps in Production (RAG + Vector DB + Caching)レッスンでコードを書いて実行できますか？
はい。すべてのLLM Apps in Production (RAG + Vector DB + Caching)レッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。