• Single-prompt attempts hit a numeric target (like a word count or readability grade) only 21.1-31.6% of the time across four commercial LLMs, in the tests reported here.
• Wrapping generation in a 5-stage loop (generate, evaluate, adjust, archive, analyze) that gates every edit with deterministic code lifted target-hit rates to 92.5-98.8%, typically within two edit rounds.
• The controller also rejects any edit that drops source entities, numbers, or keywords, keeping content fidelity at 0.92-0.93 even while forcing the numeric target.
• The authors caution the result shows reproducible control over a declared, computable metric, not that the output is validated as easier for a human reader.
• Practical recipe: declare the acceptance condition as code, restrict the model to local edits, and gate every edit on a content check.

• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.

Offers a pattern for reliably hitting numeric output constraints from LLMs by keeping the accept/reject decision in deterministic code, tested across 240 closed-loop runs on four commercial models.

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