Token limit control program
The following coping strategies are recommended for different models with token restrictions:
1. Pre-screening mechanism
- (of a computer) run
code2prompt /path --tokens -c cl100kGetting accurate counts - Comparison of limits across models: GPT-4 (32k), Claude (100k), etc.
- Special attention is paid to the cumulative consumption of multi-round dialog scenarios
2. Smart Split Program
- Split by catalog:
--include "src/utils/*" - By file type:
--include "*.py" --exclude "tests/*" - By scope of change: combined
--git-diff-branchSubmission of discrepancies only
3. Compression optimization techniques
- Delete the note:
--filter-comments(Customized templates required) - Simplify margins: add
sed 's/s+/ /g'pipe handling - Use the abbreviation: in the template with
{{truncate content length=100}}Truncate long files
emergency management
When an overrun is encountered: immediately interrupt and save progress -o partial.md, using a segmented Q&A strategy.
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 avoid token overruns when submitting code to large models?
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