MiniScript is the member of the PseudoLang family that turns the dial toward efficiency: minify the prompt by abbreviating, prioritizing the load-bearing tokens, and dropping the filler that natural language pads in. The motivation is real — every token in a long payload costs money and latency and competes for the model's attention. When you are stuffing a long policy block or a transcript into a model's context, trimming it is leverage.
Hand-minification has a sharp edge
Compress past the point where the model can reconstruct your intent, and quality falls off a cliff — silently. So treat the manual instinct as a first draft and reach for the automated, evaluated form in production. LLMLingua and LLMLingua-2 use a small model to drop low-information tokens from a prompt, reporting large compression for a small, measured accuracy cost. The lesson isn't "always compress" — it's that compression is a measurable trade you make against an evaluation, not a vibe. The manual MiniScript instinct tells you where the slack is; an evaluated compressor tells you how far you can take it before the answer degrades.
There is also a learned form worth knowing: gist-token compression trains the model to condense a prompt into a few reusable tokens — compression baked into the model rather than applied to the text.
Two rules keep compression safe
- Never compress the contract. Squeeze the context and the examples; leave the instructions, the output schema, and the refusal rules at full resolution. A garbled instruction is a different, worse failure than a garbled document.
- Measure the compressed payload, not the pretty one. The thing you ship is the minified prompt, so your evaluation set has to run against that — or you are grading a payload you do not deploy.
The takeaway
MiniScript's instinct — that most prompts carry slack you can cut — is correct, and the gains under token pressure are real. The discipline that makes it safe is to automate the cut, evaluate it, and protect the parts of the prompt that carry your intent. Minify the data; never the directives.
