Understanding Context Precision and Context Recall in RAG Systems
When building or testing Retrieval-Augmented Generation (RAG) systems, you’ll encounter two important terms: context precision and context recall. If you’re new to these, don’t worry; in this article, I’ll explain them in the simplest way possible with real-world examples.
Whether you’re a developer, QA, or just someone exploring how to evaluate LLMs effectively, understanding these two metrics will give you superpowers.
Quick Recap: What Is RAG?
In a RAG system, when a user asks a question, the system:
- Retrieves the most relevant documents from a vector database (based on similarity)
- Combines the retrieved content with the user’s question
- Sends it to an LLM (like GPT-4) to generate a final answer
But how do we know if the retrieved documents were actually useful or complete? That’s where context precision and context recall come in.
What Is Context Precision?
Context Precision measures how much of the retrieved content is actually relevant to the user’s question.
Think of it like this:
- How much of everything I pulled from the database was useful?
Example:
You asked: “What is the average cost of living for a single person in Singapore?“
The system retrieves 5 documents. After reviewing:
- 3 are about the cost of living
- 2 are about tourist attractions (not useful)
So,
- Context Precision = 3 relevant / 5 total = 0.6 or 60%
✅ High precision = Most retrieved docs were on-topic
❌ Low precision = Too much irrelevant information (noise)
What Is Context Recall?
Context Recall measures how much of the relevant content you actually found.
Think of it like this:
- Out of everything in the database that could have helped, how much did I find and use?
Example:
There are 4 good documents in the database that could answer the question. Your system only retrieved 2 of them.
So,
- Context Recall = 2 retrieved / 4 relevant = 0.5 or 50%
✅ High recall = You captured most of the useful info
❌ Low recall = You missed important pieces
TL;DR Summary:
| Metric | Think of it as. | Simple Analogy |
|---|---|---|
| Context Precision | How much of what I got was good? | How many of the books you picked were useful? |
| Context Recall | Did I get everything I needed? | Did you grab all the books that had the answer? |
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