The first thing most of us do when faced with a text classification problem is reach for the nearest large language model API. It feels like the responsible choice, the modern choice. But a question deserves a pause: what can a simple, decades-old baseline already do with the labeled data you have right now? The author measured it, and the answer is a quiet challenge to our default instincts. Before you spend time and money on a complex API call, it is worth understanding the actual return on investment that additional labels provide to a simpler model. This is not about rejecting innovation; it is about making a deliberate choice rather than a reflexive one.
This piece lands in the middle of a broader conversation about how we evaluate our tools. We often see Exploring Real-World Computer Vision: Deployments, Edge Models, and Current Challenges and assume that the latest model is the right fit for every job, but that is rarely the case. Similarly, the skills that matter in this field are shifting, as highlighted in Navigating AI/ML Job Requirements: A Shift in Expected Skills, where the ability to choose the right tool for the data is becoming more valuable than simply knowing how to call an API. The experiment is a practical reminder that our default assumptions about needing more data or more compute are often just that: assumptions. It is a call to measure, not guess.
Our take is straightforward: this is the kind of analysis that should inform your next project. If you are starting a classification task, do not begin with a prompt. Begin with your data. Run a baseline. See how far you can get with a handful of labeled examples and a simple algorithm. The trade-offs are concrete, and that is more useful than any abstract claim about model capabilities. It also connects to the broader idea that we should be exploring our options, not just following trends. You might find that the solution you already have is more capable than you thought, or you might discover the exact point where an LLM becomes worth the cost. Either way, you will have made a decision based on evidence.
The specific number that matters here is not a universal constant; it is a data point that forces you to ask the right question. How many examples do you actually need to get to a satisfactory level of performance? The answer will depend on your problem, but the method for finding it should not be a mystery. We would tell any reader who asks: run the experiment yourself. Start small, measure, and then scale. The takeaway you can quote is this: more data is not always the answer, but knowing exactly what your data is worth is the real advantage. The open question is whether you will take the time to find out before you reach for that API.