Vector embeddings solve LLM tagging at scale without fixed vocabularies
Source published ·Modelwire updated
Original coverage: Simon Willison ↗·How Modelwire adds context
The development
Doug Turnbull proposes a pragmatic workaround for LLM-based content tagging at scale. Rather than constraining models to a fixed vocabulary (which becomes unwieldy with thousands of tags), he suggests letting models generate novel tags freely, then anchoring those outputs to existing categories via vector similarity. This inverts the typical classification pipeline: instead of choosing from a predefined set, the model hallucinates plausible labels and retrieval finds the nearest canonical match. The technique sidesteps both prompt-injection risks and token-budget constraints, making it valuable for anyone managing large taxonomies or legacy content needing retroactive organization.
Modelwire’s AI-generated summary of coverage from Simon Willison.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
Turnbull's insight is that the constraint itself is the problem. Rather than fighting LLMs' tendency to generate novel outputs, he weaponizes it: let the model produce whatever tags feel right, then use vector similarity to snap them to your canonical taxonomy post-hoc. This is less about a new algorithm and more about accepting model behavior instead of fighting it.
This is largely disconnected from recent activity in the space, which has focused on either making models more obedient (constitutional AI, RLHF) or measuring their failures (jailbreaks, adversarial robustness). Turnbull's framing belongs to a quieter conversation about pragmatic deployment: how do you actually ship systems that work with LLM quirks rather than against them. It sits alongside operational work on prompt caching and token budgeting rather than capability benchmarks.
If teams managing large e-commerce or content taxonomies (Shopify, Notion, Atlassian) ship this pattern in their LLM-powered tagging features within the next 12 months, it signals this has moved from clever workaround to standard practice. If it remains a one-off technique in Doug's blog, it's a useful idea without adoption momentum.
This interpretation is generated from the summary above and available source metadata. Our methodology · Report an error
MentionsDoug Turnbull · Simon Willison · LLM
How this coverage is produced
Modelwire uses AI to generate summaries and context from source headlines, snippets, and selected archive coverage. Automated checks do not verify every claim, and items are not routinely reviewed by a person before publication. Zacaria Solis operates the site. Read the linked source for the full evidence and report errors through our corrections process.
Modelwire summarizes, we don’t republish. Simon Willison originally reported this story as “Don't classify. Hallucinate!”. The full content lives on simonwillison.net. If you’re a publisher and want a different summarization policy for your work, see our takedown page.