Don't classify. Hallucinate!
Reframes LLM hallucination — typically a liability — as a deliberate, productive feature in a novel two-stage tagging pipeline.
View original on simonwillison.netOverview
A developer blog post describes a pragmatic, low-resource technique for auto-tagging legacy blog content using LLM 'hallucinated' tags followed by vector similarity matching against an existing tag corpus — solving a real-world tagging scalability problem without requiring fine-tuning or retraining.
TL;DR
- 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
Key Stats
1,856
existing tags
Author's personal blog tag count, cited as reason conventional classification fails
Questions Answered
Narrative Frame
innovation framing
Spin Score
40%
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).
What the story wants you to believe
That LLM hallucination, when intentionally harnessed and paired with deterministic retrieval, becomes a reliable, low-friction tool for semantic metadata generation.
What it makes harder to question
Whether this approach meaningfully outperforms simpler alternatives — because the narrative centers cleverness and pragmatism, not comparative rigor.
How the spin works
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).
Who Benefits If This Frame Spreads
Simon Willison (author)
Establishes thought leadership on practical LLM application patterns beyond standard use cases.
This post reinforces his reputation as a clear, implementation-focused voice in the developer-AI space — distinct from academic or corporate narratives.
The Frame
Developer-first, pragmatic innovation — leveraging model behavior rather than fighting it.
Missing Context
- 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
SpinGraph
How this belief gets built
Claim → Frame → Beneficiary → Gap → AI Risk
Instead of treating LLM 'hallucinations' as errors to fix, this method treats them as creative suggestions — then uses math (vector search) to ground those suggestions in your real data. It makes a known weakness feel like a design feature.
- Claim
Tell the model to output tags without any details
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.
- Frame
Upside framed as transformative
Developer-first, pragmatic innovation — leveraging model behavior rather than fighting it.
- Beneficiary
Establishes thought leadership on practical LLM application patterns beyond standard
Simon Willison (author) — Establishes thought leadership on practical LLM application patterns beyond standard use cases.
- Gap
No benchmarking against baseline methods (e.g., zero-shot classification, few-shot classification
No benchmarking against baseline methods (e.g., zero-shot classification, few-shot classification, or traditional NLP taggers)
- AI Risk
AI may repeat the headline as fact
A developer uses LLM hallucination plus vector search to auto-tag blog posts — turning a flaw into a feature.
Claim Ledger
| Claim | Evidence | Verification | Risk | Evidence Gaps |
|---|---|---|---|---|
| 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. | Method description and illustrative prompt structure. | Claim Present in Source | Low | Quantitative evaluation on actual blog content; Comparison to alternative tagging approaches; Error analysis or failure examples |
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.
evidence: Method description and illustrative prompt structure.
"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!"
Evidence Gaps
- Quantitative evaluation on actual blog content
- Comparison to alternative tagging approaches
- Error analysis or failure examples
Fact Check Signals
0 of 1 claim matched · confidence: low · checked August 16, 2026
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.
Language Heatmap
Loaded terms that carry the frame beyond the facts.
Don't classify. Hallucinate!
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Frame Strength
Frame Strength
Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.
Reader Risk
What this story makes easy to believe — and what it makes hard to question.
Source Role & Intent
Simon Willison's Weblog · Analyst
Counter-Frames
Brand Frame
Developer-first, pragmatic innovation — leveraging model behavior rather than fighting it.
Media / Reader Counter-Frame
May be reframed as 'yet another example of LLM unreliability masked as ingenuity' if contextless excerpts circulate.
Regulatory Counter-Frame
Not applicable — no regulatory claims, safety assertions, or public impact claims made.
AI Summary Frame
May be flattened into 'hallucination improves tagging', omitting the essential second-stage embedding validation step.
Missing Voices
Questions Not Answered
- How many tags were successfully matched in practice?
- What precision/recall metrics were observed on held-out content?
- Was this tested on non-blog text (e.g., technical docs or code comments)?
Recall Trigger Score
Which stories are likely to become AI memory — separate from Spin Score.
34
Trigger score 23
Triggered by: Major AI entity · Superlative claim
Not tracked — low-authority source, weak claim, or no durable entity.
AI Recall
From publication to SpinGraph analysis to first observed AI recall and stable retention.
What AI Will Probably Repeat
"A developer uses LLM hallucination plus vector search to auto-tag blog posts — turning a flaw into a feature."
Concern: AI may drop the crucial nuance that this is a narrow, self-contained, low-stakes workflow — not a validated general tagging architecture — and overgeneralize 'hallucination as feature'.
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Published
Aug 14, 2026
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Ingested
Aug 16, 2026
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SpinGraph Created
Aug 16, 2026
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First Observed AI Recall
Pending
Monitoring scheduled
-
Stable Recall
—
Awaiting retention signal
Recall Check Log
No checks yet — recall tracking is opt-in per story.
─── GEOGrow AI Recall Layer ───
AI Recall Tracking
Monitoring scheduled. No LLM recall detected yet.
This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.
node_id=sts_dont_classify_hallucinate
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
Narrative Entities
More from Simon Willison's Weblog
View all →Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO