Utilizing Metadata for Better Retrieval-Augmented Generation

TL;DR

This paper systematically studies how incorporating metadata into retrieval strategies enhances retrieval-augmented generation, especially in structured corpora, by improving document distinction and embedding quality.

Contribution

It introduces and compares various metadata-aware retrieval methods, demonstrating their effectiveness over plain-text baselines and analyzing their impact on embedding space structure.

Findings

Metadata integration improves retrieval effectiveness.

Unified embeddings often outperform prefixing methods.

Metadata enhances intra-document cohesion and reduces confusion.

Abstract

Retrieval-Augmented Generation systems depend on retrieving semantically relevant document chunks to support accurate, grounded outputs from large language models. In structured and repetitive corpora such as regulatory filings, chunk similarity alone often fails to distinguish between documents with overlapping language. Practitioners often flatten metadata into input text as a heuristic, but the impact and trade-offs of this practice remain poorly understood. We present a systematic study of metadata-aware retrieval strategies, comparing plain-text baselines with approaches that embed metadata directly. Our evaluation spans metadata-as-text (prefix and suffix), a dual-encoder unified embedding that fuses metadata and content in a single index, dual-encoder late-fusion retrieval, and metadata-aware query reformulation. Across multiple retrieval metrics and question types, we find that prefixing and unified embeddings consistently outperform plain-text baselines, with the unified at times exceeding prefixing while being easier to maintain. Beyond empirical comparisons, we analyze embedding space, showing that metadata integration improves effectiveness by increasing intra-document cohesion, reducing inter-document confusion, and widening the separation between relevant and irrelevant chunks. Field-level ablations show that structural cues provide strong disambiguating signals. Our code, evaluation framework, and the RAGMATE-10K dataset are publicly hosted.