Abstract: Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through a post processing. In some examples, such a method includes operations of receiving, by a post processing engine, a response to an auto-generated prompt from the LLM that includes programming code generated by the LLM, performing a post processing of a response from the LLM that includes the programming code generated by the LLM including performing an error correction process when a term of the programming code generated by the LLM is inconsistent with terms of a schema of the user, and generating a graphical user interface (GUI) that displays the response to the auto-generated prompt when the terms of the programming code generated by the LLM including any replacement terms are consistent with the terms of the schema.

Abstract: Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through novel processes for performing retrieval augmented generation (RAG). In some examples, such a method includes operations of receiving a natural language user-provided prompt, performing a first RAG process including retrieving first RAG data including natural language, programming syntax pairings from a vector database and performing a second RAG process including retrieving second RAG data including additional programming syntax examples from a vector database that have a second level of similarity with programming syntax of the natural language, programming syntax pairings retrieved during the first RAG process. The method may also include generating an auto-generated prompt requesting generation of programming code by a large language model (LLM) and generating a graphical user interface that displays the response to the auto-generated prompt from the LLM.

Abstract: Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM). In some examples, such a method includes operations of receiving a user-provided prompt, wherein the user-provided prompt is provided in natural language, identifying an objective of the user-provided prompt, and based on the objective, providing the user-provided prompt to a first operational pipeline of a plurality of operational pipelines, wherein each operational pipeline is associated with a unique prompt template. Additionally, the method may include generating, by the first pipeline, an auto-generated prompt based on a first unique prompt template of the first pipeline, providing the auto-generated prompt to a large language model (LLM), and receiving a response to the auto-generated prompt from the LLM. A graphical user interface (GUI) may then be generated that displays the response to the auto-generated prompt.

Abstract: Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through a post processing. In some examples, such a method includes operations of receiving, by a post processing engine, a response to an auto-generated prompt from the LLM that includes programming code generated by the LLM, performing a post processing of a response from the LLM that includes the programming code generated by the LLM including performing an error correction process when a term of the programming code generated by the LLM is inconsistent with terms of a schema of the user, and generating a graphical user interface (GUI) that displays the response to the auto-generated prompt when the terms of the programming code generated by the LLM including any replacement terms are consistent with the terms of the schema.