Don't classify. Hallucinate!
Doug Turnbull proposes a "hallucinate first, match later" approach for content tagging when facing large existing tag vocabularies (e.g., 1,856+ tags) Instead of asking the LLM to classify content from a fixed tag list, the model is prompted to generate novel, never-before-seen tag classifications Vector embeddings are then used to find the closest existing tags in the corpus that match the model's hallucinated output The technique uses example prompts showing the desired tag hierarchy shape to
Analysis
TL;DR
- Doug Turnbull proposes a "hallucinate first, match later" approach for content tagging when facing large existing tag vocabularies (e.g., 1,856+ tags)
- Instead of asking the LLM to classify content from a fixed tag list, the model is prompted to generate novel, never-before-seen tag classifications
- Vector embeddings are then used to find the closest existing tags in the corpus that match the model's hallucinated output
- The technique uses example prompts showing the desired tag hierarchy shape to guide the model toward useful guesses
- This bypasses token limits and classification overload while still anchoring results to a real, existing taxonomy
Why It Matters
This approach offers a practical workaround for a common problem in AI-powered content organization: when your tag or category vocabulary is too large to fit in context, traditional classification fails. It demonstrates how leveraging LLM creativity combined with embedding-based retrieval can produce better results than forcing the model to choose from an exhaustive list.
Technical Details
- The core technique involves two stages: (1) LLM generation of novel tag classifications without reference to the existing vocabulary, and (2) vector embedding similarity search against the existing tag corpus to find the closest real matches
- The prompt includes few-shot examples showing the expected hierarchical format (e.g., "Furniture / Living Room Furniture / Coffee Tables & End Tables / Coffee Tables") to constrain the model's output shape
- The method is particularly suited for large-scale tagging problems where the full taxonomy exceeds practical context window limits
- Embedding-based nearest-neighbor matching serves as the bridge between the model's creative output and the grounded, existing classification system
Industry Insight
- This technique can be generalized beyond blog tagging to any domain requiring classification against large, evolving taxonomies (product catalogs, document management, media libraries)
- The "hallucinate then retrieve" pattern may reduce classification errors compared to forcing models to choose from overwhelming option lists, as the model can express nuanced understanding before being anchored to discrete categories
- Practitioners should consider this approach when facing tag vocabularies exceeding a few hundred entries, where traditional prompt-based classification becomes impractical
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