Recall

What it is

If you have 100 documents containing the answer to a user query, and your retrieval system only surfaces 70 of them, your recall is 0.7. It is calculated by dividing the number of true positive hits by the sum of true positives and false negatives. Unlike precision, which checks if your results are correct, recall checks if you missed anything important. In large-scale systems, achieving high recall often requires scanning millions of vectors to ensure no relevant matches are excluded.

Why it matters

Recall determines the completeness of your AI features. If you are building a legal discovery tool or a medical diagnosis assistant, low recall means your system is silently dropping critical information, which can lead to compliance failures or dangerous misinformation. Ignoring recall often results in a system that feels accurate on the surface but fails to provide answers when the user specifically asks for comprehensive results.

In practice

You manage recall by adjusting parameters like the number of nearest neighbors retrieved from a vector database or the similarity threshold for a search. If you observe that your RAG application misses obvious documents from your knowledge base, you increase the limit of retrieved chunks to cast a wider net. You then monitor this by comparing system outputs against a ground truth dataset of known expected answers.

The tradeoff

Increasing recall typically forces a decrease in precision, as casting a wider net to find all relevant items inevitably pulls in more irrelevant noise.

Where it appears

Research summaries that use Recall, each linked to its source paper.

• How AI Rewriting Hides Robot Authorship
  ARB: A Matched Authorship-Rewriting Benchmark Dataset for AI-Text Detector Evaluation

  The researchers created a new test to see if existing AI detectors can catch human writing that has been edited or rewritten by an artificial intelligence model.

• Improving Search Accuracy for AI Systems
  Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings

  The researchers developed a method to improve search accuracy in AI systems by creating synthetic questions for documents before users even ask them.

• Adapting Search to Improve Vector Retrieval
  QASP: Query-Adaptive Robust Vector Search Policy

  The researchers developed a proactive search policy that adjusts how much data it explores for every unique query, significantly reducing unnecessary work while maintaining accurate results.

• Improving Landmine Detection with Better Data
  SULAND v2: A Refined RGB Dataset and Deep Learning Object Detection Benchmark for UAV/UGV-Based SUrface LANDmine Detection Under Domain Shift

  The researchers created an improved version of a landmine detection dataset by fixing errors and setting new standards for testing how well AI models perform in different real-world environments.

• Improving AI Code Review and Accuracy
  From Code Review to Code Critique: Intent, Drift, and Spotlight for AI-Generated Diffs at Scale

  The paper introduces a new system that helps AI coding tools understand human goals and perform higher quality code reviews by focusing on essential technical standards.