Fuzzy deduplication is key to constructing large language model training corpora. However, classic Locality-Sensitive Hashing (LSH) pipelines scale poorly as corpora grow and are ill-suited to continuous ingestion. The main issue is that each new document batch must be checked against the admitted corpus before insertion. As the corpus grows, the LSH buckets grow: each query can hit several large buckets and must scan the returned candidates. To solve this problem, we present RAD (Retrieval-Augmented Deduplication), an online fuzzy deduplication system that delivers both high recall and throughput for evolving datasets. RAD maintains an incrementally updated HNSW index over admitted documents, retrieving a small, high-quality candidate neighborhood for each incoming document instead of repeatedly re-scanning the accumulated corpus. RAD is the first online fuzzy deduplication system to use HNSW, leading to stable throughput as datasets grow. However, it is not easy to maintain high recall when using HNSW-style indexes. The core issue is the distance metric between graph nodes. Jaccard similarity, the metric used for fuzzy deduplication, yields low recall when applied out-of-the-box with an HNSW index. It leads to distance score crowding, making graph traversal unreliable within a bounded number of steps. RAD addresses this with a bitmap representation that provides a more discriminative, Jaccard-aligned signal during HNSW search. Across four LLM-scale datasets (LM1B, C4, RealNews, and Common Crawl), RAD preserves the scaling trajectory needed for online fuzzy deduplication: at 30M documents, it maintains 0.94-0.97 recall relative to state-of-the-art LSH solutions, and delivers up to an 8x throughput increase.