Abstract: An adaptive answer-generating agent implemented on a computer system, communicatively connected to a user device, and configured to: receive a user query from the user device; map the user query to one of a plurality of reconfigurable answer-generation schemas stored in first data storage, each of the answer-generation schema specify a sequence of steps to generate an answer output to the query, said agent including an answer generation engine configured to: receive the answer-generation schema mapped to the user query; implement the sequence of steps to generate an answer output, and communicate the answer output to the user device, wherein the agent further includes a schema modification function configured to: receive schema modification data from a further computing device relating to one or more of the answer-generation schemas, and modify the sequence of steps of the one or more of the answer-generation schemas in accordance with the schema modification data.

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 method of lighting a panel made of resin absorbing material is taught along with a lay up for carrying out the method. The method involves fixing a light generating means, preferably fiber optics, within a layer of the lay up which may be resin absorbing or non resin absorbing, rigid, flexible or semi-rigid in different embodiments. The method involves flowing resin from an inlet opening into a sealed mold which may be as little as a shaping layer and a vacuum bag. The lay up is comprised of at least one top layer of absorbent or reinforcing material which defines a passage or hole from the inlet opening to a non-absorbent layer which non-absorbent layer lies above at least one bottom layer of reinforcing material. At least one of these layers contains the light means, which are preferably fiber optic fibers. The fibers where necessary are protected in a tube prior to filling the lay up with resin at the point of entry into the mold to protect them.

Abstract: A method of flowing resin from an inlet opening into a sealed mold and a lay up which is comprised of at least one top layer of absorbent or reinforcing material which defines a passage or hole from the inlet opening to a non-absorbent layer which non-absorbent layer lies above at least one bottom layer of reinforcing material. This process flows the resin from the inlet opening into the mold then through the passage defined by the top reinforcing layer through the non-absorbent layer and then through channels defined by the non-absorbent layer throughout the lay up.

Abstract: A method of flowing resin from an inlet opening into a sealed mold and a lay up which is comprised of at least one top layer of absorbent or reinforcing material which defines a passage or hole from the inlet opening to a non-absorbent layer which non-absorbent layer lies above at least one bottom layer of reinforcing material. This process flows the resin from the inlet opening into the mold then through the passage defined by the top reinforcing layer through the non-absorbent layer and then through channels defined by the non-absorbent layer throughout the lay up.

Abstract: A method of flowing resin from an inlet opening into a sealed mold and a lay up which is comprised of at least one top layer of absorbent or reinforcing material which defines a passage or hole from the inlet opening to a non-absorbent layer which non-absorbent layer lies above at least one bottom layer of reinforcing material. This process flows the resin from the inlet opening into the mold then through the passage defined by the top reinforcing layer through the non-absorbent layer and then through channels defined by the non-absorbent layer throughout the lay up.