How to Get Relevant Chunks for Recall@k and Precision@k in RAG

2026-05-10 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Evaluating Retrieval-Augmented Generation (RAG) systems with Recall@k and Precision@k requires pre-defining and identifying relevant chunks, a critical step often overlooked. The article highlights that "relevance" is not fixed and must be explicitly defined for a given system, impacting metric outcomes and optimal 'k' values. It details two primary methods for obtaining relevant chunks: manual labeling, which creates a gold standard and clarifies relevance definitions for 50-100 queries, and a hybrid approach combining manual labeling with LLMs and heuristics for scalability. While manual labeling offers accuracy and ground truth, it struggles with scale and system evolution. The hybrid method addresses scalability by using LLMs for labeling based on manually derived rules, but it introduces challenges like lack of ground truth, inconsistency, and potential bias if used exclusively. The article emphasizes an iterative loop between manual labeling, rule definition, LLM labeling, evaluation, and refinement of rules/prompts/chunking strategies.

Key takeaway

For MLOps Engineers optimizing RAG systems, defining "relevant" chunks is foundational for meaningful Recall@k and Precision@k metrics. You should establish a small, high-quality manual labeling dataset to define your system's specific relevance rules. Then, scale this process using a hybrid approach with LLMs, continuously iterating on your relevance definitions and LLM prompts to ensure consistent and accurate evaluation as your system evolves.

Key insights

Defining relevance is crucial for accurate RAG retrieval evaluation using Recall@k and Precision@k.

Principles

• Relevance is system-dependent, not fixed.
• Manual labeling establishes ground truth and relevance rules.
• Hybrid approaches combine accuracy with scalability.

Method

A hybrid pipeline involves creating a manual seed dataset, defining explicit relevance rules, and then using an LLM to label additional chunks based on these rules for scaled evaluation.

In practice

• Start with 50-100 manually labeled queries.
• Iterate on relevance definitions and LLM prompts.
• Compare human vs. LLM labels to validate.

Topics

• RAG Evaluation
• Recall@k
• Precision@k
• Relevance Labeling
• Manual Labeling

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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• You’re Measuring RAG Wrong: 5 Metrics That Actually Matter — Traditional RAG metrics like Recall@K are insufficient; focus on production-relevant metrics tied to user value.
• RAG Is Not Dead. You’re Just Building It Wrong. — Optimizing RAG for production requires systematic engineering across all layers, prioritizing retrieval over LLM changes.
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• When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering — Iterative RAG's staged retrieval and reasoning process outperforms static ideal evidence in complex multi-hop QA.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.