[Demo] How to re-categorize content at scale using LLMs

Key Insights

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  Manual retagging of 1,200 blog posts would take about 10 hours, but leveraging GPT-4 reduced active human time to about 2 hours.

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  Using GPT-4 via command line and shell scripts enables automated tagging outside typical chat interfaces.

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  An organically grown taxonomy over 20 years contained unclear acronyms and inconsistent tag forms that GPT initially struggled with.

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  Cleaning and standardizing the taxonomy before prompting GPT is critical for effective AI assistance.

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  A review step of AI-suggested tags in CSV format allows human correction to avoid hallucinations entering production.

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  GPT-4 can propose new and useful tags outside the original taxonomy, enriching content classification.

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  The four-step GRU framework (Gather, Review, Update, Wrap up) balances automation with human oversight.

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  Storing blog content as markdown files simplifies integrating AI workflows via scripting and file manipulation.

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  The approach is adaptable and scalable to other CMS platforms by replacing scripting with API calls.

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  Taxonomies should use clear, unambiguous terms to improve both human and AI understanding.

Notable Quotes