Drafting meta descriptions with an LLM without losing control: a review loop in 60 lines of Python

说明与用途

使用LLM自动生成元描述，包含长度、重复、禁用短语和声明验证检查，随后人工批准后发布到CMS。

问题

Writing meta descriptions for hundreds of pages can produce long, generic, duplicated, or misleading text.

结果

The pipeline produces 140‑155 character meta descriptions that avoid banned phrases, duplicates, and unsupported claims, with human approval ensuring quality before CMS publication.

工作流

• Input: CSV with URL, title, H1, text
• Draft: LLM generates one description per page using only that page’s text
• Automatic checks: length, banned phrases, duplicates, grounding against source text
• Human review: spreadsheet with draft, flags, approve/edit column
• Publish: approved rows are sent to CMS

证据

Writing meta descriptions for a few hundred pages is a classic task to hand to a language model.

来源

The trick is not the prompt. It is the checks around it and a human approval step.

来源

Length, duplicates, banned phrases, and "is every claim in the source text?".

来源