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LLM Apps in Production (RAG + Vector DB + Caching) · Pelajaran

Mengembangkan Tolok Ukur Evaluasi

Buat kumpulan data dan tolok ukur khusus untuk menguji serta membandingkan berbagai konfigurasi dan peningkatan RAG secara sistematis.

Mengembangkan Tolok Ukur Evaluasi adalah pelajaran LLM Apps in Production (RAG + Vector DB + Caching) gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar LLM Apps in Production (RAG + Vector DB + Caching), dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus LLM Apps in Production (RAG + Vector DB + Caching) mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

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!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Mengembangkan Tolok Ukur Evaluasi” gratis?

Ya — teks lengkap “Mengembangkan Tolok Ukur Evaluasi” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus LLM Apps in Production (RAG + Vector DB + Caching), upgrade ke CoddyKit PRO. Kursus LLM Apps in Production (RAG + Vector DB + Caching) mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Mengembangkan Tolok Ukur Evaluasi”?

Buat kumpulan data dan tolok ukur khusus untuk menguji serta membandingkan berbagai konfigurasi dan peningkatan RAG secara sistematis. Kamu berlatih LLM Apps in Production (RAG + Vector DB + Caching) dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai LLM Apps in Production (RAG + Vector DB + Caching)?

Tidak diperlukan pengalaman sebelumnya. LLM Apps in Production (RAG + Vector DB + Caching) di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Mengembangkan Tolok Ukur Evaluasi” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran LLM Apps in Production (RAG + Vector DB + Caching) ini?

Ya. Setiap pelajaran LLM Apps in Production (RAG + Vector DB + Caching) menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Metrik Utama untuk Kinerja RAG
  2. Mengembangkan Tolok Ukur Evaluasi
  3. Pengujian A/B dan Siklus Umpan Balik Pengguna
  4. Mendeteksi dan Mengukur Halusinasi
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