Intent-preserving compression
Keeps role, constraints, format, and examples while removing filler.
Shrink your prompts without losing intent. Cut token spend up to 60% on every API call, across every major AI model.
Free plan available · No credit card required
Keeps role, constraints, format, and examples while removing filler.
See exact token counts before/after and per-model pricing impact.
Compressed prompt previewed for each major model and tokenizer.
Push compression further when you need maximum savings.
Save compressed variants alongside originals — fork and version freely.
Drop compressed prompts into workflows for end-to-end cost savings.
Production apps making millions of calls see immediate cost reduction.
Trim bloated system messages without breaking behavior.
Compress examples while keeping signal density.
Reduce tool descriptions and context overhead in agent loops.
Drop any prompt — system, user, or full chain.
See the token diff and intent comparison.
Store the compressed version to your library or pipe into a workflow.
Join builders using InstructFlow AI to optimize prompts, chain steps, and share reusable workflows.
A technique that rewrites a prompt to use fewer tokens while preserving the original intent, constraints, and expected output.
Typical compression ratios range 30–60% depending on the original prompt. Highly verbose prompts often compress more.
Done well, no. InstructFlow AI's compressor preserves role, constraints, examples, and output format — it removes redundancy and filler.
Compression works for any text-in model: OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, and more.
Rewrite ChatGPT prompts for clarity, structure, and reliable output.
Build XML-structured prompts tuned for Anthropic Claude models.
Design multi-step AI workflows visually with chained prompts.
Outbound, follow-up, and proposal workflows for revenue teams.
Source, screen, and outreach prompts for talent teams.
From idea to hook to script to thumbnail in one chained flow.