The phase-ordering problem—finding optimal pass sequence—remains NP-hard. While recent RL approaches have shown improved results, they impose heavy per-input search costs. We introduce RAPO, a retrieval-augmented phase-ordering framework that replaces online RL exploration with similarity-based retrieval. Offline, RAPO embeds LLVM IR with IR‑BERT, clusters programs via k-means, and stores each cluster’s representative RL‑discovered pass sequences in a sequence cache. During compilation, a new program is embedded, mapped into its nearest cluster, and optimized by retrieving the cached sequence, thereby transforming “per-program search“ into “similarity-based retrieval.“ RAPO is model-agnostic (compatible with PPO, DQN, and ε-greedy, etc.) and includes lightweight fallbacks for corner cases. RAPO achieves up to ~18.6% of IR instruction count reduction over -Oz, matching or outperforming RL baselines, while reducing phase-ordering search overhead up to ~177x. These results suggest that RAPO delivers near–per‑input quality with deployment‑grade efficiency by transforming online phase ordering into fast, similarity‑driven retrieval.