Answer assistance computing system

A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising:

• generating a message embedding corresponding to a representation of a most recent message received by a user from a conversation history record;
• for a plurality of passages within one or more documents in a knowledge base, determining a set of passages relevant to the representation of the most recent message, each passage having a relevance to the representation of the most recent message based on a computed semantic similarity of the message embedding to a passage embedding corresponding to the passage;
• programmatically generate an answer-generation input instruction for a language model to cause the language model to produce an answer output, the answer-generation input instruction generated based at least on the representation of the most recent message, the set of passages, and an answer-format instruction;
• causing a representation of the answer output to be presented via a user interface (UI) of a computing device;
• and
• causing a representation of an augmented-answer output to be presented via the UI of the computing device by:
• for a plurality of messages previously sent by the user, determining a set of messages similar to the answer output, each message having a similarity to the answer output based on a computed string similarity of the message to the answer output;
• and
• programmatically generate an answer-augmentation instruction for the language model to cause the language model to produce the augmented-answer output, the answer-augmentation instruction generated based at least on the answer output and the set of messages.

A computing system comprising:

• a processor;
• and
• a non-transitory computer-readable medium having stored thereon instructions that when executed by the processor, cause the processor to perform operations including:
• accessing a conversation history record;
• generating a message embedding corresponding to a representation of a most recent message received by a user from the conversation history record;
• for a plurality of question and answer (Q&A) pairs determined from previous conversation history records, determining a Q&A pair relevant to the representation of the most recent message based on a computed semantic similarity of the message embedding to a question embedding corresponding to a corresponding question of the Q&A pair;
• programmatically generate an answer-generation input instruction for a language model to cause the language model to produce an answer output, the answer-generation input instruction generated based at least on the representation of the most recent message, a corresponding answer of the Q&A pair, and an answer-format instruction;
• causing a representation of the answer output to be presented via a user interface (UI) of a computing device;
• and
• causing a representation of an augmented-answer output to be presented via the UI of the computing device by:
• for a plurality of messages previously sent by the user, determining a set of messages similar to the answer output, each message having a similarity to the answer output based on a computed string similarity of the message to the answer output;
• and
• programmatically generate an answer-augmentation instruction for the language model to cause the language model to produce the augmented-answer output, the answer-augmentation instruction generated based at least on the answer output and the set of messages.