Methods and apparatus for generating intelligible data segments with large language models

A method, comprising:

• determining, using a processor of a first compute device and without human intervention, a plurality of statistically significant variables in a cluster associated with a first data set based on a statistical significance test including variables in at least one of the first data set or a plurality of second data sets, the cluster being associated with a consumer type;
• generating, using the processor and without human intervention, a set of pseudosentences associated with the cluster based on a high value and a low value for each statistically significant variable from the plurality of statistically significant variables;
• sending, without human intervention, a signal to cause the set of pseudosentences to be inserted into a first no-code prompt for a large language model to predict a segment description for the consumer type associated with the cluster, the first no-code prompt having a code-style to reduce a variance of the segment description relative to output from the large language model based on a second no-code prompt without the code-style, the segment description being a representation of relationships among each statistically significant variable from the plurality of statistically significant variables based on the set of pseudosentences;
• and
• sending, from the first compute device and to a second compute device, data representing the segment description.

A non-transitory, processor-readable medium, storing instructions that, when executed by a processor, cause the processor to:

• send, to a user compute device, a signal to cause a display at the user compute device to display a plot representing a first plurality of clusters in a first data set, each cluster from the first plurality of clusters being associated with a consumer type;
• receive, from the user compute device, a signal indicative of a request associated with an evaluation of the plot;
• input, without human intervention, the first data set to a machine learning model to generate a second plurality of clusters in the first data set;
• determine, without human intervention, a plurality of statistically significant variables in a cluster from the second plurality of clusters based on a statistical significance test for variables in at least one of the first data set or a plurality of second data sets;
• generate, without human intervention, a set of pseudosentences associated with the cluster from the second plurality of clusters based on an in-group value and an out-group value for each statistically significant variable from the plurality of statistically significant variables;
• send, without human intervention, a signal to cause the set of pseudosentences to be inserted into a first no-code prompt for a large language model to predict a segment description for the consumer type associated with the cluster from the second plurality of clusters, the first no-code prompt having a code-style to reduce a variance of the segment description relative to output from the large language model based on a second no-code prompt without the code-style, the segment description being a representation of relationships among each statistically significant variable from the plurality of statistically significant variables based on the set of pseudosentences;
• and
• send, to the user compute device, data representing the segment description for the cluster from the second plurality of clusters.