When you understand the technical building blocks behind generative AI, its potential uses go far beyond what we already see.
With the right programming and training, LLMs can summarize information, label and categorize data, answer questions and locate specific information. For retailers specifically, the potential use cases span from front-end marketing applications to back-end merchandising support and more.Current e-commerce models require lots of human intervention and manual labor to maintain essential features like site search and product catalogs.
For example, Google’s next-generation search engine, which is built on LLMs, can display hyper-personalized search results, going beyond demographics like “men's shirts” and “women’s shirts” to display shirts of specific styles and brands based on an individual customer’s specific preferences.
For example, an application could be built to analyze historical campaigns and customer data to determine what creative and copy resonated best with certain customer segments. These insights would then be passed to an LLM to create collateral specific to targeted demographics. Performance data is then fed back into the application to refine its results.
For example, to train a model to generate product pages, the model would first need to be fed a training dataset of tens of thousands of existing product pages. The model then analyzes this data to detect patterns and build connections between different data points. Engineers then have to prompt the model to create its own examples, and with the feedback from each prompt, it further refines its answers until it can reliably create approved output.
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