Large Language Models (LLMs) are increasingly used in software engineering tasks due to their strong performance across diverse applications. In this paper, we ask a fundamental and novel question: \textit{To what extent can LLMs replace traditional machine learning pipelines that rely on labeled data, feature engineering, and retraining?}

Our intuition is that many long-standing approaches in software engineering can be reimagined through the lens of reasoning rather than training. Unlike conventional pipelines that learn statistical patterns from data, LLMs can directly reason about contextual consistency using their pretrained knowledge. To illustrate this idea, we revisit a well-known anomaly detection pipeline (CHABADA for Android apps) and show how its clustering and retraining stages can be replaced with a simple prompting strategy. The result is a streamlined, zero-shot workflow that leverages semantic reasoning without labeled datasets, feature extraction, or retraining.

Our goal is not to propose a new tool, but to highlight a broader paradigm: LLMs open the door to reinterpreting established ML-based workflows as reasoning pipelines. This perspective suggests a path toward lighter-weight, training-free alternatives for many specialized software engineering tasks.