Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs

Controlling the length of text produced by large language models (LLMs) remains challenging: models frequently overshoot or undershoot explicit length instructions because they cannot reliably keep an internal token count. We present a prompt-based, one-shot strategy that compels an off-the-shelf LLM to generate exactly a desired number of tokens--words (English) or characters (Chinese)--without any fine-tuning or iterative sampling. The prompt appends countdown markers and explicit counting rules so that the model "writes while counting." We evaluate on four settings: open-ended generation (1-1000 tokens), XSUM summarization, MT-Bench-LI instruction following, and the LIFEBENCH equal-length track. On MT-Bench-LI, strict length compliance with gpt-4.1 leaps from below 30 % under na ıve prompts to above 95 % with our countdown prompt--surpassing the popular draft-then-revise baseline--while judged answer quality is preserved. These results demonstrate that precise length control can be achieved through prompt engineering alone, offering a lightweight alternative to training-or decoding-based methods.