Solving a 350-Image Classification Problem with GPT-4

A small AI company tackled a challenging image recognition problem: identifying 350 highly similar car illustrations. Traditional computer vision and augmented reality approaches failed. The team tried MobileNet transfer learning and data augmentation, but results were inconsistent. Ultimately, they cleverly combined a KNN-based image embedding search with GPT-4, submitting candidate images to GPT-4 for final matching. While not perfect, this solution significantly improved accuracy and successfully powered a museum app, even improving the company's main product line. This demonstrates how large language models are increasingly becoming versatile tools in product development, simplifying the AI application process.