Leveraging large language models to craft meaningful synthesis of the underlying trends and patterns in a certain segments

A computer-program product comprising one or more non-transitory machine-readable storage media, including stored instructions configured to cause a computing system to perform a set of actions including:

• accessing a hierarchy of a multi-dimensional data, wherein the hierarchy comprises one or more ancestor nodes and two or more descendant dimension nodes;
• iterating over nodes in the hierarchy as base nodes to detect anomalies or patterns for the base nodes, wherein the iterating comprises:
• detecting a first anomaly or pattern for a first descendant dimension of a selected base node;
• and
• detecting a second anomaly or pattern for a second descendant dimension of the selected base node;
• generating a data structure that encapsulates:
• a first identity of a member of the first descendant dimension that fits the first anomaly or pattern,
• one or more values of the first descendant dimension that fit the first anomaly or pattern,
• one or more characteristics of the first anomaly or pattern,
• a second identity of a member of the second descendant dimension that fits the second anomaly or pattern,
• one or more values of the second descendant dimension that fit the second anomaly or pattern, and
• one or more characteristics of the second anomaly or pattern;
• generating a prompt comprising the data structure and a request to summarize the data structure;
• prompting a large language model with the prompt to generate a resulting summary of a plurality of patterns or anomalies;
• and
• causing display of at least part of the resulting summary of the plurality of patterns or anomalies.

A system comprising:

• one or more processors;
• one or more non-transitory computer-readable media storing instructions, which, when executed by the system, cause the system to perform a set of actions including:
• accessing a hierarchy of a multi-dimensional data, wherein the hierarchy comprises one or more ancestor nodes and two or more descendant dimension nodes;
• iterating over nodes in the hierarchy as base nodes to detect anomalies or patterns for the base nodes, wherein the iterating comprises:
• detecting a first anomaly or pattern for a first descendant dimension of a selected base node;
• and
• detecting a second anomaly or pattern for a second descendant dimension of the selected base node;
• generating a data structure that encapsulates:
• a first identity of a member of the first descendant dimension that fits the first anomaly or pattern,
• one or more values of the first descendant dimension that fit the first anomaly or pattern,
• one or more characteristics of the first anomaly or pattern,
• a second identity of a member of the second descendant dimension that fits the second anomaly or pattern,
• one or more values of the second descendant dimension that fit the second anomaly or pattern, and
• one or more characteristics of the second anomaly or pattern;
• generating a prompt comprising the data structure and a request to summarize the data structure;
• prompting a large language model with the prompt to generate a resulting summary of a plurality of patterns or anomalies;
• and
• causing display of at least part of the resulting summary of the plurality of patterns or anomalies.