Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.

CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.

Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.

OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs. These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.

For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the 'Speech' service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.

In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.

Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model's internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .

Fine-tuning, on the other hand, refers to the process of retraining a pretrained model on a smaller, task-specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively 'specializing' the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .

Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.