You are developing a multimodal AI model that combines video data with sensor readings to monitor and predict equipment failures in an industrial setting. The model outputs a probability score indicating the likelihood of failure. However, your stakeholders are struggling to interpret these scores. Which visualization approach would be most effective in conveying the likelihood of equipment failure and the contributing factors?

While testing a multimodal AI model designed to generate captions for images, you observe that the generated captions are often too literal, failing to capture the nuances or context of the images. Which strategies should you consider to improve the quality of the generated captions? (Select two)

You are tasked with developing a Retrieval-Augmented Generation (RAG) system for a chatbot that helps users troubleshoot technical issues. The chatbot needs to understand queries about a wide range of topics and respond with accurate, contextually relevant information. Which approach would best improve the chatbot's performance in handling diverse queries?

You are developing a multimodal model that integrates audio, text, and image data for a sentiment analysis task. During training, you observe that the model’s loss function is fluctuating significantly, particularly when fusing the different modalities. Which technique is most likely to improve the stability of the model during training?

In a scenario where an AI system is designed to automate document processing by classifying, summarizing, and extracting key information from large volumes of legal documents, which combination of techniques leveraging pretrained modern LLMs would be most effective?