LLM Error Analysis Prompts for Large Code Log Context
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Solution Overview
Problem
Existing large language models (LLMs) struggle to efficiently analyze complex software code errors due to size limitations and inefficiencies in handling large error logs, often producing inaccurate or nonsensical outputs when insufficient context is provided.
Innovation Solution
A system that generates prompts tailored to specific code errors by identifying relevant context from logs and documents, ensuring the prompts do not exceed size limits, and utilizes LLMs to provide accurate explanations and suggested fixes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a Large Language Model analyzes a large corpus of code or error logs, then comprehensive error analysis is achieved, but the prompt size exceeds model limits and processing efficiency decreases
Solution Approach 1:
The system segments the large error log into multiple smaller chunks, each containing a specific error and its surrounding context. Each chunk is processed separately by the LLM, preventing prompt size from exceeding model limits while maintaining comprehensive error analysis across the entire log file.
Solution Approach 2:
The system extracts only the relevant error portions and their immediate context from the large error log, removing unrelated content. This extraction process creates compact prompts that fit within model size limits while retaining all necessary information for accurate error analysis.
2Measurement precision
If a Large Language Model is provided with generic or insufficient context, then prompt size is reduced, but analysis accuracy decreases and hallucinations increase
Solution Approach 1:
The system enhances each error chunk with localized context specifically relevant to that error, such as surrounding code lines, function definitions, and related log entries. This local quality enhancement ensures each prompt contains sufficient context for accurate analysis without including unnecessary global information that would bloat the prompt size.
Solution Approach 2:
The system performs preliminary processing to identify and attach only the most relevant context to each error before generating the prompt. This preliminary action ensures that each prompt contains pre-selected, high-value context that maximizes analysis accuracy while maintaining compact size.
3Reliability
If all context from error logs is provided to the LLM, then comprehensive analysis is possible, but processing time increases and model limits are exceeded
Solution Approach 1:
The system extracts only the essential error information and immediately relevant context from the log, removing redundant and unrelated content. This extraction enables comprehensive error log analysis to be performed through multiple small, fast processing iterations rather than one slow, large processing task.
Data Source
AI summary
Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving or accessing a log comprising an error message, the error message indicating an error in code; determining the error message from the log; determining a context associated with the error; generating a prompt for a large language model (“LLM”), the prompt comprising at least: the error message, and the context associated with the error; transmitting the prompt to the LLM; and receiving an output from the LLM in response to the prompt, the output comprising at least: an explanation of the error message, and a suggested fix for the error.