RAG Evaluation-1

🚀 Evaluating RAG Systems: Measuring Context Precision

When we talk about Retrieval-Augmented Generation (RAG), the real magic lies in how well the system can find the right pieces of information and then use them to answer a question.

But how do we know if it’s really doing a good job?
👉 That’s where Context Precision comes in.

📑 Table of Contents

1. 🔎 What is Context Precision?
2. 📘 Example
3. 📊 Why Does This Matter?
4. 📝 Key Takeaways
5. 💡 Final Thoughts

🔎 What is Context Precision?

In simple terms, context precision measures:

➡️ Out of all the documents the system retrieved, how many were actually useful in producing the answer?

It’s very similar to searching on Google:
👉 If you click on the top 3 results and only 1 of them helps, the precision is 1/3 = ~33%.

📘 Example

Suppose we ask:
“How many articles are there in the Selenium WebDriver Python course?”

The system answers:
✅ “There are 23 articles in the course.”

It also retrieves 4 chunks of documents:

📌 Calculating Precision:

• If we only consider the first 3 chunks: 1 out of 3 useful → Precision = 33%
• If we consider all 4 chunks: 1 out of 4 useful → Precision = 25%

📊 Why Does This Matter?

• ✅ A high precision score means the system is pulling back highly focused and relevant context.
• ⚠️ A low precision score means the system is pulling in noise — irrelevant chunks that don’t help the answer.

By measuring context precision, we can evaluate and improve the retrieval step of RAG, ensuring the AI is always grounded in the right evidence.

📝 Key Takeaways

• 📌 RAG doesn’t retrieve full documents; it retrieves chunks (paragraphs or sections).
• 📌 Precision = Useful chunks ÷ Total chunks considered.
• 📌 This metric helps us judge how well the system is focusing on the right information.

💡 Final Thoughts

Think of it like this:
🔍 Context precision tells us how many of the “search results” the AI actually used to get to the right answer.