Use GPT-4 because suggesting product names from a textual description is a natural-language generation task. The application can supply the product’s characteristics, intended audience, tone, and naming constraints in a prompt, and GPT-4 can generate candidate names that reflect those requirements. GPT models interpret natural-language instructions and generate corresponding textual completions, making them appropriate for ideation, summarization, content drafting, and conversational applications.
The other model categories serve different modalities. DALL-E is an image-generation model that creates visual content from natural-language prompts; it is not intended primarily to produce product-name lists. Whisper is a speech-recognition model used to transcribe audio into text. Embedding models convert text into numerical vectors for similarity search, clustering, classification, and retrieval scenarios; they do not generate human-readable product names. Microsoft’s Foundry model documentation distinguishes chat and generative models from image-generation, audio, and embedding model families.
The app should therefore deploy GPT-4, construct a prompt containing the product description and naming criteria, and consume the generated textual response through the model inference endpoint.
Study Guide alignment: Select and deploy appropriate generative models, integrate deployed models into applications, and implement prompt-driven content-generation workflows.
