AI Code Prompt Context Selection Under Length Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing code generation systems lack efficiency and accuracy in providing relevant recommendations based on user inputs, particularly when dealing with complex programming tasks.
Innovation Solution
A method and electronic device utilizing a generative AI model to generate code by obtaining user inputs, generating prompts, selecting context information based on priority, and transmitting these prompts to a server for code generation, thereby enhancing the relevance and accuracy of code recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all available context information is included in the prompt for code generation, then the completeness and accuracy of code recommendations is improved, but the length of the prompt increases excessively, leading to reduced efficiency and potential loss of information
Solution Approach 1:
The patent segments context information into multiple categories (e.g., project-level context, file-level context, function-level context) and processes them hierarchically. This allows the system to organize vast amounts of context information in a structured manner, selecting and transmitting only the most relevant segments to the generative AI model, thereby maintaining accuracy while managing prompt length effectively
Solution Approach 2:
The patent extracts essential context information from the codebase by analyzing dependencies, imports, and code structures. It identifies and extracts only the critical context needed for accurate code generation, removing redundant or less relevant information. This extraction process ensures that the prompt contains high-value information without excessive length
2Measurement precision
If extensive context information is provided to ensure accurate code generation, then the quality of code recommendations is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis and organization of context information before generating code recommendations. It pre-processes the codebase to identify relevant files, functions, and dependencies, and structures this information in advance. This preliminary action reduces the computational burden during actual code generation, improving response time while maintaining quality
Solution Approach 2:
The patent dynamically adjusts parameters such as the depth of context analysis, the number of files to include, and the level of detail in context information based on the specific generation task. By changing these parameters adaptively, the system optimizes the balance between code quality and processing time for different scenarios
3Reliability
If the system processes and analyzes all available code context before generating recommendations, then the relevance and accuracy of code suggestions is improved, but the system complexity and computational overhead increase
Solution Approach 1:
The patent applies different processing strategies to different parts of the context information based on their relevance and importance. Critical context (e.g., directly related files, imported modules) receives detailed analysis, while less critical context receives simplified processing or is excluded entirely. This local quality approach maintains relevance without uniformly high complexity across all context
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
Provided are an electronic device and a method implemented by the electronic device for providing code by using a generative artificial intelligence (AI) model. The method may include obtaining a user input corresponding to a first document, based on the user input, obtaining first context information available for code generation, based on the first context information and the user input, generating a first prompt for the code generation, based on a length of the first prompt, selecting, from the first context information, second context information according to priority information, based on the second context information and the user input, generating a second prompt corresponding to the first prompt, transmitting the first prompt or the second prompt to a server; and receiving, from the server, recommended code generated through the generative Al model based on the first prompt or the second prompt and providing the recommended code.