Abstract: Systems and methods for generating a slide using machine learning (ML) are provided. The system may obtain content of a new slide, cause a large language model (LLM) to generate a slide layout for the new slide content, and determine a subset of template slides. The LLM may cause a template recommendation model to select a template slide best suited to display the content from the subset of template slides. The LLM may cause a layout-mapping model to generate template slide element mapping describing elements of the template slide. The LLM may cause a slide generator model to generate the new slide by generating a mapping of the content to the one or more elements of the template slide, and populating the one or more elements with the content based upon the mapping. The system may display the new slide at a user interface of a computing device.

Abstract: The systems and methods described herein relate to improvements to generative artificial intelligence systems through the use of generative artificial intelligence pipelines to supply external information to pre-trained large language models for use in answering queries. To improve the efficiency and accuracy of large language models in responding to user queries, according to various aspects described herein, such queries may be modified and augmented with additional relevant information from decentralized data sources. The retrieval-augmented generation pipeline may interface with one or more decentralized data sources and retrieve the external information, including unstructured text and/or structured data.

Abstract: The systems and methods described herein relate to improvements to generative artificial intelligence systems through the use of generative artificial intelligence pipelines to supply external information to pre-trained large language models for use in answering queries. To improve the efficiency and accuracy of large language models in responding to user queries, according to various aspects described herein, such queries may be modified and augmented with additional relevant information from decentralized data sources. The retrieval-augmented generation pipeline may interface with one or more decentralized data sources and retrieve the external information, including unstructured text and/or structured data.

Abstract: The systems and methods described herein relate to improvements to generative artificial intelligence systems through the use of generative artificial intelligence pipelines to supply external information to pre-trained large language models for use in answering queries. To improve the efficiency and accuracy of large language models in responding to user queries, according to various aspects described herein, such queries may be modified and augmented with additional relevant information and may be divided into multiple queries for parallel handling, the results of which may then be combined into a response. The additional relevant information may include portions of documents or other data sets to be used in generating the response. Additional aspects may further improve resilience and flexibility by managing the generation or implementation of such modified and augmented queries.

Abstract: The systems and methods described herein relate to improvements to generative artificial intelligence systems through the use of generative artificial intelligence pipelines to supply external information to pre-trained large language models for use in answering queries. To improve the efficiency and accuracy of large language models in responding to user queries, according to various aspects described herein, such queries may be modified and augmented with additional relevant information and may be divided into multiple queries for parallel handling, the results of which may then be combined into a response. The additional relevant information may include portions of documents or other data sets to be used in generating the response. Additional aspects may further improve resilience and flexibility by managing the generation or implementation of such modified and augmented queries.

Abstract: The systems and methods described herein relate to generative artificial intelligence systems using retrieval-augmented generation pipelines to supply information to large language models (LLMs). The potential for failures by such LLMs to return responses to prompts significantly increases with system complexity. To improve the resilience of the pipelines in handling such failures, various aspects described herein provide mechanisms for early detection and remediation of such prompt failure events. Thus, prompt failure events may be identified based upon (i) an elapsed time between sending a prompt and receiving a first token from the LLM exceeding a first threshold or (ii) an elapsed time between receiving such first token and receiving a last token exceeding a second threshold. Remediation may be achieved by causing a copy of the failed prompt to be sent to the LLM, without waiting for an error from the LLM provider or a standard network request timeout.