Detection and correction of errors in a database using classifier and generative artificial intelligence models

A non-transitory computer-readable storage medium storing instructions, that when executed, cause a processor to perform, steps comprising:

• receiving an indication to autonomously review an item database;
• retrieving exposure data for a set of items, wherein the exposure data comprises data about one or more user interactions with the items in the set of items;
• inputting the exposure data to a detection system, wherein the detection system comprises a machine learning model that is trained to output, for each item associated with exposure data input to the detection system, an indication of whether an attribute associated with the item is erroneous;
• receiving, from the detection system, a set of indications, wherein each indication corresponds to a respective item of the set of items and indicates whether an attribute of the respective item is erroneous;
• prompting a correction system to correct each erroneous attribute described by the indications in the set of indications, wherein the correction system comprises a generative model, and wherein the prompt includes information about each item stored in the item database, one or more images of each item associated with one of the erroneous attributes, and a request to suggest a correction for the each erroneous attribute;
• receiving a set of suggested corrections from the generative model;
• and
• updating each erroneous attribute in the item database according to the set of suggested corrections.

A system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed, cause the processor to perform steps comprising:
• receiving an indication to autonomously review an item database;
• retrieving exposure data for a set of items, wherein the exposure data comprises data about one or more user interactions with the items in the set of items;
• inputting the exposure data to a detection system, wherein the detection system comprises a machine learning model that is trained to output, for each item associated with exposure data input to the detection system, an indication of whether an attribute associated with the item is erroneous;
• receiving, from the detection system, a set of indications, wherein each indication corresponds to a respective item of the set of items and indicates whether an attribute of the respective item is erroneous;
• prompting a correction system to correct each erroneous attribute described by the indications in the set of indications, wherein the correction system comprises a generative model, and wherein the prompt includes information about each item stored in the item database, one or more images of each item associated with one of the erroneous attributes, and a request to suggest a correction for the each erroneous attribute;
• receiving a set of suggested corrections from the generative model;
• and
• updating each erroneous attribute in the item database according to the set of suggested corrections.