Query Auto Completion (QAC) plays a pivotal role in streamlining search engine interactions for users. By suggesting relevant query completions, QAC acts as a gateway to efficient search formulation, allowing users to swiftly express their search intent. Traditional QAC systems rely on curated tries derived from historical query logs to provide timely and context-aware query completions. In this study, we explore the integration of Large Language Model (LLM)-generated query completions into the QAC system. Broadly, the huge inference latency of LLMs renders them unsuitable as an online tool for improving quality of QAC which require rapid response times. Hence, our study investigates query completions using LLMs within two offline contexts: local intent and trending queries. We leverage GPT-3.5 to generate query completions and incorporate these completions into our existing production system. The goal is to improve relevance and address the coverage gap by providing relevant query completions to users. Over an extended period, we rigorously assessed user engagement with these GPT-driven completions by leveraging metrics such as click-through rates and key strokes saved per query. Our insights shed light on how effectively GPT provided relevant query completions to users. Furthermore, we investigate the positive influence of GPT-generated completions on users’ acceptance rates, contributing to the effectiveness of GPT-based query completions in real-world query auto-complete scenarios.