What is the difference between Context Recall and Context Precision in RAG metrics?
❓ Interview Prompt: Compare retrieval quality metrics: Context Recall (ground truth coverage) vs Context Precision (signal-to-noise ratio).
Retrieval metrics measure how effectively a vector database fetches documents required to answer a question:
1. Context Precision:
Measures the ratio of relevant chunks to total retrieved chunks. High precision means top-ranked retrieved chunks contain zero noisy, irrelevant text.
$$\text{Context Precision} = \frac{\text{Relevant Chunks Retrieved}}{\text{Total Chunks Retrieved}}$$
2. Context Recall:
Measures whether all ground-truth facts required to answer the question were successfully retrieved.
$$\text{Context Recall} = \frac{\text{Ground Truth Facts Found in Retrieved Chunks}}{\text{Total Ground Truth Facts}}$$
Code Implementation Example
function calculateContextPrecision(retrievedChunks: { isRelevant: boolean }[]): number {
const relevantCount = retrievedChunks.filter(c => c.isRelevant).length;
return relevantCount / retrievedChunks.length;
}Context Precision measures signal-to-noise ratio of retrieved chunks; Context Recall measures ground-truth fact coverage.