Context Optimization Solution
Improving the effectiveness of big models for understanding code context can be done in three ways:
1. Structured inputs
- Utilize the tool's auto-generated Source Tree to show the project architecture.
- pass (a bill or inspection etc)
--git-log-branchAdd version change history - utilization
--git-diff-branchContrasting Branch Differences
2. Template customization
Create Handlebars templates (.hbs files) to control the output structure:
- built-in variable
source_treecap (a poem)filesOrganizational content - Example template:
templates/document-the-code.hbs - Customized notes can be added to describe key modules
3. Intelligent filtering
- expense or outlay
--include "*.py"Focus on specific languages - Cooperative projects
.gitignoreAutomatic exclusion of irrelevant files - pass (a bill or inspection etc)
--no-ignoreOverride default filtering rules
This answer comes from the articlecode2prompt: converting code libraries into big-model comprehensible prompt filesThe
May not be reproduced without permission:AI productivity tools " How to optimize the effect of contextual understanding when analyzing code using big models?
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