Abstract—[Problem] As industries increasingly depend on complex software systems, efficient log analysis is essential for maintaining reliability and privacy. However, Identifying problems through logs is often time-consuming and costly for developers. [Background] Large language models (LLMs) can automate parts of log analysis, but challenges like limited computational resources and the frequent need to retrain LLMs due to the dynamic nature of software logs persist. External LLMs, such as GPTs, along with in-context learning techniques, can help reduce some of these issues, but other challenges, including token limitations, high token costs, and data privacy, remain. [Method] To tackle these challenges, we developed an automated pipeline that extracts log files and employs in-context learning, allowing the model to efficiently adapt to changes without extensive retraining. Our approach introduces a novel flame-graph-like method that reduces token usage, thereby lowering token-related costs and response latency while maintaining high accuracy. [Results] This solution allows industries to automate log analysis, minimize system downtime, and enhance performance, all while keeping data privacy and maintaining operational efficiency. [Conclusion] Our flame-graph-like methodology reduces input tokens by 93.61% and processing latency by 77.45%. Our anonymization results show an improvement of 138.63% over the baseline. This industrial experience report presents our approach to allow industries to balance token costs, maintain response accuracy, and ensure data privacy while relying on external LLMs without the need to manage computational resources directly.