When GPT-3 first launched, its massive scale sparked excitement about general-purpose language AI. Yet many production tasks still require fine-tuning or domain expertise. Early developers discovered that few-shot prompting alone rarely matches specialised models on accuracy.
The base model excels at fluent text generation and creativity. However it struggles with numerical precision, reasoning and domain specifics. Later versions like GPT-3.5 and GPT-4 improved reliability but also increased costs.
Today teams evaluate whether GPT-3 is sufficient or if smaller models or fine-tuning offer better trade-offs. Understanding prompt design, context windows and evaluation metrics is key before relying on the API.