AI Program Generation Using Compiler-Gathered Code Semantics
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Solution Overview
Problem
Existing AI models limit the amount of contextual information that can be included with a request, making it difficult to determine all relevant content about target code, which can incur substantial cost and time, and conventional techniques are inefficient and resource-intensive.
Innovation Solution
A computer program is generated using compiler-gathered semantic information, allowing it to determine information about target code without input limitations, by converting a user-generated request into an AI prompt and utilizing semantic information gathered by a compiler at compile time through an API.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all relevant contextual information is provided to the AI model, then the accuracy of code information determination is improved, but the processing cost and time consumption increase substantially
Solution Approach 1:
The patent performs preliminary indexing of the codebase using embeddings before the AI model needs to query it. This pre-processing step organizes the codebase into a searchable structure with metadata, so that when information is needed, the system can quickly locate relevant portions without having to process the entire codebase at query time. This resolves the contradiction by preparing the data structure in advance, enabling fast retrieval of relevant contextual information.
Solution Approach 2:
The patent extracts only the relevant contextual information from the codebase based on the specific query, rather than providing all code to the AI model. The system uses the indexed embeddings to identify and extract only the portions of code that are relevant to the user's request, significantly reducing the amount of information the AI model needs to process while maintaining accuracy.
2Measurement precision
If all relevant contextual information is provided to the AI model, then the accuracy of code information determination is improved, but the processing cost increases substantially
Solution Approach 1:
The patent extracts only the relevant contextual information from the codebase based on the specific query, rather than providing all code to the AI model. The system uses the indexed embeddings to identify and extract only the portions of code that are relevant to the user's request, significantly reducing the amount of information the AI model needs to process while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary indexing of the codebase using embeddings before the AI model needs to query it. This pre-processing step organizes the codebase into a searchable structure with metadata, so that when information is needed, the system can quickly locate relevant portions without having to process the entire codebase at query time. This resolves the contradiction by preparing the data structure in advance, enabling fast retrieval of relevant contextual information.
3Loss of information
If embeddings are used to find relevant content in the codebase, then the relevance of contextual information is improved, but not all relevant content can be found and substantial cost is incurred
Solution Approach 1:
The patent performs preliminary indexing of the codebase using embeddings before the AI model needs to query it. This pre-processing step organizes the codebase into a searchable structure with metadata, so that when information is needed, the system can quickly locate relevant portions without having to process the entire codebase at query time. This resolves the contradiction by preparing the data structure in advance, enabling fast retrieval of relevant contextual information.
Solution Approach 2:
The patent introduces an intermediary indexing layer between the codebase and the AI model. The embeddings index acts as a mediator that pre-processes and organizes the codebase, allowing the system to efficiently query and retrieve relevant information without directly burdening the AI model with the entire codebase. This intermediary structure enables complete retrieval of relevant content at low cost.
Data Source
AI summary
Techniques are described herein that are capable of performing AI-based generation of a computer program using compiler-gathered semantic information about target code. A user-generated request that requests information about target code is converted into an AI prompt, which requests that the AI model generate a computer program to determine the information. An AI model is caused to generate the computer program, which comprises configuring the computer program to determine, at runtime of the computer program, the information using semantic information about the target code gathered by a compiler and provided to the computer program by an API, by providing the AI prompt as an input to the AI model. A response to the AI prompt that includes the computer program is received from the AI model. Presentation of a representation of the computer program and/or automatic execution of the computer program against the target code is triggered.