In today’s competitive digital marketing landscape, clear brand messaging and targeted audience engagement are crucial for success. Sphere’s Marketing Copy is your go-to AI-powered tool for generating text and images simultaneously in seconds.
In this installment of “Under the Hood,” we delve into system prompts, the first of three technologies that fine-tune both image and text creation to resonate with your marketing campaigns.
System prompts act as higher-level guidelines that shape the general behavior of both latent text-to-image diffusion models and LLMs. These meta-level prompts are distinct from generic prompts as they define the model’s role and thereby guiding and standardizing the overall outputs across various tasks. In contrast, generic prompts are usually more task specific and focused on getting a single specific output.
By providing a framework to the instructions given, users are able to instruct the AI more comprehensively by neatly dividing up prompts into multiple components that each serve a specific goal.
For Image generation models, we often utilize both positive (what we aim for the image to encapsulate) and negative prompts (elements). In text generation models, the model operates within a composite framework, informed by both system and user-generated messages. This dual-input approach helps prime the model by defining its role and outlining task specifics and anticipated outcomes.
While defining the role of the AI, we can further elevate quality. Trained on a vast corpus of data, most image and text generation models have an intrinsic understanding of how we quantify quality and hence, have specifiers and prompts you can use to signal it.
For instance, when crafting prompts for social media content generation, we position the AI not merely as a digital marketing aide but as a master strategist hailing from an elite advertising agency. In the realm of visuals, invoking terms like ‘4K’ or ‘Unreal Engine’ can achieve hyper-realistic details, enriching your imagery.
It’s essential, however, to acknowledge that the AI’s training dataset exerts a considerable influence on outputs and may inadvertently expose inherent biases. Stay engaged for our next “Under the Hood” episode, where we’ll explore this issue and outline our strategies for mitigating it.