RAG Recall V/s Precision

Imagine you’re asking your school librarian for books about World War II. Whether they bring you all the right books (recall) or only the useful ones (precision) makes a huge difference in how well you study.

Now replace the librarian with an AI system — that’s what happens in RAG (Retrieval-Augmented Generation).

📑 Table of Contents

  1. 📚 The Librarian Analogy
  2. 🔎 What is Recall in RAG?
  3. 🎯 What is Precision in RAG?
  4. 🌍 Real-World RAG Example
  5. 📊 Side-by-Side Comparison
  6. 📌 Key Takeaways

📚 The Librarian Analogy

  • Recall = Did the librarian bring all the right books?
  • Precision = Did the librarian avoid bringing irrelevant books?

Same idea applies to AI when it searches documents before answering your question.

🔎 What is Recall in RAG?

In RAG, Recall means: ➡️ Out of all the useful documents in the knowledge base, how many did the AI manage to retrieve?

  • High recall = AI fetched all the key documents.
  • Low recall = AI missed something important.

🎯 What is Precision in RAG?

In RAG, Precision means: ➡️ Out of all the documents the AI retrieved, how many were actually useful for answering the question?

  • High precision = AI brought only relevant information.
  • Low precision = AI added a lot of noise (irrelevant text).

🌍 Real-World RAG Example

Let’s say a student asks an AI tutor:

“How many articles are there in the Selenium WebDriver Python course?”

  1. The AI looks into its knowledge base.

  2. It retrieves 3 documents:

    • Document A: Course overview with 23 articles ✅
    • Document B: Introduction to Selenium ✅
    • Document C: Blog post about Python ❌

Evaluation:

  • Recall 🔎: The answer (23 articles) was found → recall is good.
  • Precision 🎯: Only 2 out of 3 were helpful → precision is lower.

📊 Side-by-Side Comparison

🧩 Concept 👍 High Value Means 👎 Low Value Means 📚 Librarian Example 🤖 RAG Example
Context Recall 🔎 You got all the important material. You missed some key material. Librarian brought all the World War II books. AI retrieved the doc that contains 23 articles.
Context Precision 🎯 You got only the useful material. You got too much irrelevant stuff. Librarian brought 10 books, but only 2 are about World War II. AI retrieved 3 docs, but 1 was irrelevant.

📌 Key Takeaways

  • Recall = Don’t miss important info.
  • Precision = Don’t include irrelevant info.
  • In RAG, the best systems balance both → fetch all the right documents, and only the right ones.

💬 If you had to choose, would you prefer your AI assistant to bring everything (high recall) or only the best (high precision)?