Desenvolvendo Referências de Avaliação
Crie conjuntos de dados e referências personalizados para testar e comparar sistematicamente diferentes configurações e melhorias de RAG.
Desenvolvendo Referências de Avaliação é uma aula grátis de LLM Apps in Production (RAG + Vector DB + Caching) no CoddyKit. Esta é a aula 2 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de LLM Apps in Production (RAG + Vector DB + Caching), e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de LLM Apps in Production (RAG + Vector DB + Caching) inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
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!
Perguntas Frequentes
A aula “Desenvolvendo Referências de Avaliação” é grátis?
Sim — o texto completo de “Desenvolvendo Referências de Avaliação” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de LLM Apps in Production (RAG + Vector DB + Caching), atualize para CoddyKit PRO. O curso de LLM Apps in Production (RAG + Vector DB + Caching) inclui 4 aulas no total.
O que vou aprender em “Desenvolvendo Referências de Avaliação”?
Crie conjuntos de dados e referências personalizados para testar e comparar sistematicamente diferentes configurações e melhorias de RAG. Você pratica LLM Apps in Production (RAG + Vector DB + Caching) com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar LLM Apps in Production (RAG + Vector DB + Caching)?
Nenhuma experiência prévia é necessária. LLM Apps in Production (RAG + Vector DB + Caching) no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 2 de 4.
Quanto tempo leva a aula “Desenvolvendo Referências de Avaliação”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de LLM Apps in Production (RAG + Vector DB + Caching)?
Sim. Cada aula de LLM Apps in Production (RAG + Vector DB + Caching) inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Principais Métricas de Desempenho do RAG
- Desenvolvendo Referências de Avaliação
- Testes A/B e Ciclos de Feedback dos Usuários
- Detectando e medindo alucinações