The paper "Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4" by Xuchao Zhang et al. from Microsoft addresses the challenge of Root Cause Analysis (RCA) in cloud service incident diagnosis. The authors propose an in-context learning approach to automate RCA without the need for fine-tuning, which is costly and resource-intensive. They evaluate their method using a dataset of 100,000 production incidents from CompanyX, comparing it with fine-tuned large language models (LLMs) like GPT-3. The results show that their in-context learning approach outperforms fine-tuned models by an average of 24.8% across multiple metrics, with a significant 49.7% improvement over the zero-shot model. Human evaluation involving actual incident owners further demonstrates the superiority of the in-context learning approach, achieving a 43.5% improvement in correctness and an 8.7% enhancement in readability. The study highlights the effectiveness of using vanilla LLMs like GPT-4 for RCA tasks, avoiding the high computational and maintenance costs associated with fine-tuning. The paper also explores various research questions, including the impact of in-context example relevance and ordering on performance.The paper "Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4" by Xuchao Zhang et al. from Microsoft addresses the challenge of Root Cause Analysis (RCA) in cloud service incident diagnosis. The authors propose an in-context learning approach to automate RCA without the need for fine-tuning, which is costly and resource-intensive. They evaluate their method using a dataset of 100,000 production incidents from CompanyX, comparing it with fine-tuned large language models (LLMs) like GPT-3. The results show that their in-context learning approach outperforms fine-tuned models by an average of 24.8% across multiple metrics, with a significant 49.7% improvement over the zero-shot model. Human evaluation involving actual incident owners further demonstrates the superiority of the in-context learning approach, achieving a 43.5% improvement in correctness and an 8.7% enhancement in readability. The study highlights the effectiveness of using vanilla LLMs like GPT-4 for RCA tasks, avoiding the high computational and maintenance costs associated with fine-tuning. The paper also explores various research questions, including the impact of in-context example relevance and ordering on performance.