Intermediate Representation Workflow for Dependency-Aware LLM Code Generation
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Solution Overview
Problem
Large language models (LLMs) face limitations in generating correct and formatted compilable computer code, particularly for proprietary or complex code types, and scaling issues arise due to token limits and increased processing time and costs.
Innovation Solution
Generate an intermediate representation using an LLM and a separate programmatic component to compile it into compilable code, breaking requests into smaller parts based on feature dependencies and using a feature tree to manage complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLM is used to directly generate compilable computer code, then code generation speed is improved, but code accuracy and formatting correctness deteriorate
Solution Approach 1:
The code generation process is segmented into two distinct stages: first generating an intermediate representation (IR) that captures the logical structure, then separately compiling it into final compilable code. This segmentation allows the LLM to focus on creative logic generation while a dedicated compiler handles syntax correctness, resolving the contradiction between speed and accuracy.
Solution Approach 2:
An intermediate representation (IR) is introduced as a mediator between the LLM's natural language understanding and the target compilable code. The IR serves as a structured bridge that preserves the LLM's generative speed while enabling precise syntactic transformation through programmatic compilation, thereby maintaining both productivity and manufacturing precision.
2Adaptability or versatility
If LLM processes large complex requests with many features, then code coverage is improved, but processing time and costs increase
Solution Approach 1:
Complex code generation requests are segmented into multiple independent intermediate representation files, each handling specific features or modules. This segmentation enables parallel processing of different code components, reducing overall processing time while maintaining comprehensive code coverage for large-scale projects.
Solution Approach 2:
The system performs preliminary analysis to identify feature dependencies and generates a processing plan before actual code generation. By pre-organizing the generation sequence and identifying independent modules, the system can process complex requests more efficiently, reducing processing time while ensuring all features are covered.
3Adaptability or versatility
If LLM generates code for proprietary or complex code types, then code functionality is improved, but formatting correctness deteriorates
Solution Approach 1:
The intermediate representation serves as a format-agnostic mediator that captures the functional logic independent of target code formatting requirements. This allows the LLM to generate correct functionality once in IR form, which can then be compiled into any proprietary or complex code type with proper formatting through dedicated compilation processes.
Solution Approach 2:
The formatting and syntax-specific requirements are extracted from the generation process and handled separately by the compilation stage. The LLM focuses solely on generating the functional logic in intermediate representation, while the compiler extracts and applies the specific formatting rules for the target code type, ensuring both functionality and formatting correctness.
4Productivity
If LLM generates all code components in one pass, then generation speed is improved, but error rate increases
Solution Approach 1:
The code generation is segmented into intermediate representation generation and compilation phases. Errors are more easily detected and corrected in the structured IR phase before compilation, reducing the overall error rate while maintaining generation speed through the modular two-phase approach.
Solution Approach 2:
The compilation phase provides feedback on syntax and formatting errors that can be traced back to the intermediate representation. This feedback loop allows for systematic error detection and correction without requiring complete regeneration, maintaining productivity while improving reliability.
Data Source
AI summary
In an example embodiment, a feature tree of all features in a request is generated. This feature tree describes interrelations between features that are dependent upon one another. The request is then broken into multiple, smaller requests. More particularly, each feature is included in its own dedicated request. Based on the dependencies, the smaller requests are then sent to the LLM in an order that preserve the dependency relationships. Intermediate results generated by the LLM in response to the multiple smaller request are then merged into a single intermediate result that can then be passed to the programmatic component for compiling.