One potential limitation of the VLM-LLM prompting approach is the reliance on pre-trained language and vision models, which may not capture the nuances of specific industrial datasets. To address this limitation and enhance the generalization capabilities of the proposed framework, domain-specific fine-tuning of the VLM and LLM models can be performed on a diverse set of industrial datasets. By fine-tuning the models on a range of data distributions and defect types, the models can learn to extract relevant features and improve their performance on unseen data.
Another limitation is the interpretability of the prompts generated for the VLM and LLM models. To address this, a systematic approach to prompt engineering can be developed, incorporating domain knowledge and feedback mechanisms to refine the prompts over time. By iteratively improving the prompts based on model performance and domain expertise, the models can better capture the key features of the industrial datasets and enhance their defect classification capabilities.