Problem-specific
Code generation compression
Code generation AI sends file contents, specifications, and coding guidelines with every request. Most of this context is irrelevant to the specific code being generated.
Code generation context bloat
A code generation request typically includes: the current file being edited (200-2,000 tokens), related files (500-5,000 tokens), project structure (200-500 tokens), coding standards (200-500 tokens), and the user's request (50-200 tokens). Only ~30% of these tokens are relevant to the specific code change.
Frequently asked questions
Does compression affect code quality?
No. SuperCompress keeps the code context relevant to the task. Generated code quality is maintained.
Can I use it with GPT-4 for code generation?
Yes. Compression works with any model used for code generation.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.