Cheaper LLM labelling (entropicthoughts.com)

A developer recently shared a cost-effective approach for labeling software commits as either “maintenance” or “new development” using a lightweight language model (LLM), specifically the GPT-5.6 Luna. Initially tested on a small dataset with satisfactory results, the developer rolled out a script that efficiently processes a large batch of 21,000 commits while leveraging the LLM to improve classification accuracy. The implementation calls the LLM for each commit, although it introduces latency of about 1.5 seconds per request. To address this, the developer implemented a "fast classifier" based on logistic regression, which swiftly handles obvious cases, reducing reliance on the LLM and thus optimizing both speed and cost. The significance of this hybrid approach lies in its ability to combine computational efficiency with accuracy, effectively expediting the classification process by around 46%. By utilizing features derived from the commit messages and employing a simple logistic regression model to determine initial labels, the developer illustrates a novel framework for enhancing LLM applications beyond high-cost, resource-intensive operations. This not only provides a practical solution for developers managing extensive repositories but also reflects broader implications for machine learning applications, emphasizing the importance of balancing model complexity with operational efficiency.