Large language models (LLMs) can be overkill for simple text classification tasks that require straightforward, deterministic outcomes, such as determining whether a message is a lead or not. The use of LLMs in such scenarios can lead to high costs, slower response times, and non-deterministic outputs, without leveraging user feedback to improve the model. By replacing the LLM with a simpler system using sentence embeddings and an online classifier, the process becomes more efficient, cost-effective, and responsive to user feedback, with the added benefit of complete control over the learning loop. This highlights the importance of choosing the right tool for the task, reserving LLMs for tasks requiring complex reasoning or handling ambiguous language.
