Applying FSL to improve brand consistency
In our previous exploration of ZSL in Chapter 9, we demonstrated how a pre-trained model like GPT-4 could generate marketing copy for an e-commerce brand launching eco-friendly products. This ZSL approach, while powerful, primarily relied on the model’s ability to infer context and content from generalized pre-training using prompts emphasizing terms like sustainable and eco-friendly.
As the examples in this section will show, while ZSL provides a solid foundation for generating brand-relevant content, it often lacks the precision required for capturing the deeper, more nuanced aspects of a brand’s ethos. This is particularly true for brands whose identity is heavily tied to specific practices or principles that may not be well represented in the generalized training of the large language model (LLM).
Steps for effective FSL in marketing campaigns
- Prompt refinement and execution: Begin with a basic...
