Privacy-preserving prompt engineering for generative artificial intelligence

A computing system comprising:

• memory;
• one or more hardware processors coupled to the memory;
• and
• one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising:
• receiving a prompt query entered through a user interface;
• detecting sensitive data in the prompt query that violates a security protocol;
• generating a modified prompt query which anonymizes the sensitive data;
• submitting the modified prompt query to a large language model;
• receiving a reply generated by the large language model, wherein the reply contains anonymized sensitive data;
• generating a modified reply which deanonymizes the anonymized sensitive data;
• and
• presenting the modified reply on the user interface.

One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method, the method comprising:

• receiving a prompt query entered through a user interface;
• detecting sensitive data in the prompt query that violates a security protocol;
• generating a modified prompt query which anonymizes the sensitive data;
• submitting the modified prompt query to a large language model;
• receiving a reply generated by the large language model, wherein the reply contains anonymized sensitive data;
• generating a modified reply which deanonymizes the anonymized sensitive data;
• and
• presenting the modified reply on the user interface,
• wherein generating the modified prompt query comprises combining the prompt query with a context query, wherein the context query is configured to instruct the large language model to generate a script written in a specified programming language in the reply.