Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment

Query Auto-Completion (QAC) is a critical feature of modern search systems that improves search efficiency by suggesting completions as users type. However, existing approaches face fundamental challenges: traditional retrieve-and-rank pipelines have poor long-tail coverage and require extensive feature engineering, while recent generative methods suffer from hallucination and safety risks. We present a unified framework that reformulates QAC as end-to-end list generation through Retrieval-Augmented Generation (RAG) and multi-objective Direct Preference Optimization (DPO).

Our approach combines three key innovations:

1. Reformulating QAC as end-to-end list generation with multi-objective optimization;
2. A comprehensive methodology combining RAG, multi-objective DPO with learned and rule-based verifiers, and iterative critique-revision for high-quality synthetic data;
3. A hybrid serving architecture enabling efficient production deployment under strict latency constraints.

Evaluation on a large-scale commercial search platform demonstrates substantial improvements: offline metrics show gains across all dimensions, human evaluation yields +0.40 to +0.69 preference scores, and a controlled online experiment achieves 5.44% reduction in keystrokes and 3.46% increase in suggestion adoption, validating that unified generation with RAG and multi-objective alignment provides an effective solution for production QAC.

This work represents a paradigm shift to end-to-end generation powered by large language models, RAG, and multi-objective alignment, establishing a production-validated framework that can benefit the broader search and recommendation industry.

• † University of California, Berkeley