Summary & Key Takeaways ​

• Traditional LLM tagging struggles with large, predefined tag vocabularies.
• Doug Turnbull's method suggests letting the LLM generate novel, "hallucinated" tags.
• These imagined tags are then mapped to existing concrete tags using vector embeddings.
• This approach avoids overwhelming the LLM with a massive tag list.
• It leverages the LLM's generative capabilities for better tag suggestions.
• An example prompt demonstrates how to guide the model's output format.

Our Commentary ​

I've been wrestling with old blog content and its lack of proper tags for ages. This idea of letting the LLM just imagine tags, then using embeddings to find the closest match in my existing vocabulary? That's genuinely brilliant. It sidesteps the context window limits and feels so much more organic than trying to force a classification. We're definitely trying this out for our own archives. It's a smart hack.