Don't classify. Hallucinate! | SpinGraph: Innovation framing
SpinGraph analysis of Simon Willison's Weblog's Don't classify. Hallucinate! story: innovation framing, The Hype, Spin Score 40%, moderate AI repetition risk.
Know the moment AI knows your story. Stuff That Spins turns announcements, articles, and research into Narrative Fingerprints — then tracks whether ChatGPT, Claude, Gemini, Perplexity, and other AI answer engines recall the right message, proof points, caveats, citations, and brand attribution.
hallucination, vector embeddings, tagging, LLM prompting, developer workflow
Uses LLMs not for classification but for generative tag suggestion ('hallucination') Matches generated tags to existing vocabulary via vector embeddings, not exact string match Solves real developer pain point: scaling metadata curation across 1,856 existing tags without API cost or prompt engineering overload
Emphasizes cleverness and novelty of the approach while minimizing discussion of failure modes (e.g., hallucinated tags too semantically distant for embedding retrieval to recover, or domain drift between generated and corpus tags).
Developer-first, pragmatic innovation — leveraging model behavior rather than fighting it.
A developer uses LLM hallucination plus vector search to auto-tag blog posts — turning a flaw into a feature.
No benchmarking against baseline methods (e.g., zero-shot classification, few-shot classification, or traditional NLP taggers); No discussion of latency, token cost, or embedding index maintenance overhead
The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as hallucinate, neat solution, novel. The distribution reads as editorial reporting. A pressure point: No benchmarking against baseline methods (e.g., zero-shot classification, few-shot classification, or traditional NLP taggers).
Tell the model to output tags without any details of the existing vocabulary, then use vector embeddings against the existing corpus to find the concrete tags that are closest to the ones the model imagined might fit.
Doug Turnbull has a neat solution. Tell the model to output tags without any details of the existing vocabulary, then use vector embeddings against the existing corpus to find the concrete tags that are closest to the ones the model imagined might fit!
Extracted statistics from the source narrative
Author's personal blog tag count, cited as reason conventional classification fails