Abstract: A computer system for responding to user queries. The system includes: a coordinating agent configured to receive from a source device a user query, and a constrained set of function-implementing agents, each function-implementing agent configured to perform a function. Upon receipt of the user query, the coordinating agent is configured to: formulate a plan to generate a response to the user query, the plan including a sequence in which one or more of the function-implementing agents will be invoked to perform their associated function, and execute the plan by controlling the function-implementing agents in accordance with the sequence to generate the response to the user query. The constrained set of function-implementing agents includes a closed group of function-implementing agents.

Abstract: A computer implemented method for generating a response to a received electronic message. A generative AI is prompted to analyse the electronic message and to generate intent data indicative of an intent associated with the electronic message and data-identifier data indicative of one or more predetermined data identifiers present in the electronic message. The electronic message is analysed to identify and retrieve client data associated with one or more parties associated with the electronic message. The method includes Verifying the retrieved client data includes data corresponding to the generated data-identifier data. If so, query an ERP system with the verified data-identifier data to extract ERP data corresponding to the predetermined data identifiers of which the data-identifier data is indicative, select a response-message template using the intent data, populate the selected response-message template with the ERP data, and present the populated response-message template to a user for approval.

Abstract: A computer implemented method for generating a response to a received electronic message. A generative AI is prompted to analyse the electronic message and to generate intent data indicative of an intent associated with the electronic message and data-identifier data indicative of one or more predetermined data identifiers present in the electronic message. The electronic message is analysed to identify and retrieve client data associated with one or more parties associated with the electronic message. The method includes Verifying the retrieved client data includes data corresponding to the generated data-identifier data. If so, query an ERP system with the verified data-identifier data to extract ERP data corresponding to the predetermined data identifiers of which the data-identifier data is indicative, select a response-message template using the intent data, populate the selected response-message template with the ERP data, and present the populated response-message template to a user for approval.

Abstract: A system and computer implemented method for detecting hallucination in output of a generative AI system. User input is received specifying a query or task relating to information contained in a data object. A first vector representation of the user input and a second vector representation of the data object are generated. The first and second vector representations are compared to identify parts of the data object which match the query or task. An input is generated for a generative AI system with the user input and the parts of the data object. The input is input to a generative AI system. An output produced by the generative AI system is analysed to determine if the output contains information also present in the data object. If not, an error process is initiated. If so, the output is produced by the generative AI system.

Abstract: A computer-implemented chatbot including a prompt generation module, a large language model (LLM) module, and an answer generation module. The prompt generation modules generates an initial prompt based on a prompt template combined with a received user query. The initial prompt includes information source data specifying sources of factual information, conversation history data, and failed response data. The initial prompt is input to the LLM module, which is configured to generate an output and communicate the output to the answer generation module. The answer generation modules determines if the output is a plan to answer a user query. If so, relevant data is retrieved from external database or more APIs specified in the information source data. A further prompt is generated for answering the user query and input to the LLM module. The answer generation module repeats its tasks until a suitable answer to the user query is output.

Abstract: A computer implemented method of detecting hallucination in a large language model (LLM) output. A message is received. A prompt is generated for an LLM including the message and an instruction to generate an output identifying predetermined content in the message. The prompt is passed through an LLM to generate the output. The output is processed in accordance with a hallucination detection process to identify if any predetermined content identified by the LLM in the output is potentially hallucinated.

Abstract: A system and computer implemented method for generating validated prompt-templates for generating prompts for instructing large language models (LLMs) to perform specific tasks. An initial prompt is generated instructing an LLM to produce a plurality of candidate prompt-templates. The initial prompt includes an input data type to be included with a prompt generated using a candidate prompt-template, and output data to be produced by an LLM that has processed a prompt generated using a candidate prompt-template. The initial prompt is passed through an LLM to generate candidate prompt-templates. Test prompts are generated, each constructed from a candidate prompt-templates using input data from a set of pre-labelled input data having items of input data and corresponding labels. Each test prompt is passed through a further LLM to generate an output. The output is assessed, and candidate-prompt-templates are selected for subsequent generation of prompts.