Shorter Answers Start With a Clear Output Contract

A model can answer the question correctly and still spend unnecessary tokens getting there. Repeated context, ceremonial introductions, and unsolicited alternatives all increase output without necessarily improving usefulness. The most reliable starting point is an explicit output contract: tell the model exactly what the reader needs, how much space it has, and where to stop.

This walkthrough focuses on reducing generated text without stripping away essential reasoning, uncertainty, or safety information. The goal is a complete answer with less overhead, not the shortest possible answer.

Step one defines the smallest useful deliverable

Start with the decision or action the response must support. A developer diagnosing a failed request may need the likely cause and the next diagnostic step, rather than a tutorial on networking. A reviewer may need only blocking defects, rather than a paragraph praising each correct implementation detail.

Write that deliverable into the request. Ask for the likely cause, one supporting observation, and one next step. This gives the model a concrete stopping point. Merely asking it to be concise leaves the meaning of concise largely unspecified.

Step two replaces vague brevity with boundaries

Add a realistic length target and a simple structure. For a routine explanation, request one paragraph of roughly eighty words. For a comparison, request three decision criteria and a recommendation. Choose limits that fit the task rather than imposing the same ceiling everywhere.

Word limits are approximate instructions, not exact token controls. Tokenization varies with language, punctuation, and content. A provider's output limit supplies a harder boundary, but reaching it can truncate an otherwise useful answer. Treat that limit as a guardrail, not a substitute for a well-scoped request.

Step three removes predictable sources of filler

Identify material your application consistently does not need. Common examples include restating the question, announcing the answer, repeating the recommendation at the end, and offering unrelated follow-up help. Exclude those explicitly when they add no value.

Keep the instruction compact. A long catalogue of prohibited phrases can consume more input tokens than it saves on a small response. One sentence asking for the answer directly, without introductions or repetition, is often a better starting point than an elaborate style policy.

Step four preserves details that change decisions

Brevity fails when it removes a prerequisite, hides uncertainty, or turns a conditional recommendation into an absolute claim. Tell the model to retain assumptions and caveats that could change the user's next action. Ask it to omit background that does not affect that action.

For technical troubleshooting, this might mean keeping a compatibility condition while dropping a general explanation of the protocol. For a risky operation, essential precautions belong in the answer even when the preferred format is short. Compress repetition before compressing correctness.

Step five measures savings across representative requests

Compare the original and revised instructions on the same sample of real tasks. Record provider-reported input and output tokens, then evaluate completeness, correctness, and the need for follow-up questions. Shorter first answers are not savings if users must repeatedly ask for missing details.

BestRelay, which publishes this article, is a quality-first API relay that forwards each request to the official model named in it without substitution. It supports model families including Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, and Grok, and works with Claude Code, Codex, Gemini CLI, and OpenAI SDK clients. Keep the selected model fixed while testing instructions so a routing or model change does not confound the comparison.

Step six turns successful tests into defaults

Promote the best-performing output contract into your application's standard instructions, with exceptions for tasks requiring more detail. Retest after substantial prompt or model changes. Track total cost rather than output length alone: longer instructions add input tokens, and cache behavior can change the economics.

BestRelay uses pay-as-you-go billing based on provider-reported token counts, with cache reads and writes priced separately. Members can also earn referral commissions when invited users top up; keep those earnings separate from measurements of token efficiency. Create a BestRelay account to test your concise output instructions.

A good default is specific enough to prevent filler and flexible enough to preserve the answer. Save tokens by defining completion clearly, then verify that less text still does the job.