SPIN Processed
Source Simon Willison's Weblog simonwillison.net Analyst Center
August 14, 2026 developer workflow developer

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.net

Overview

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

What happened?Who is involved?Why does this matter?

Narrative Frame

innovation framing

The Hype

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

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside primary

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

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.

  1. 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.

  2. Frame

    Upside framed as transformative

    Developer-first, pragmatic innovation — leveraging model behavior rather than fighting it.

  3. 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.

  4. 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)

  5. 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

01 Primary Technical Claim Present in Source risk:Low

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

No direct fact-check match found

0 of 1 claim matched · confidence: low · checked August 16, 2026

01 No direct match

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.

Fact Check Signals

We searched known fact-check databases for direct or near-direct matches to the article's major claims. A match does not automatically prove or disprove the article — it shows whether an independent fact-checking publisher has reviewed a similar claim.

  • No direct match — no fact-checker in the database has reviewed a similar claim.
  • Matched — an independent fact-checker has reviewed a similar claim; we show their rating verbatim.
  • Conflicting coverage — fact-checkers disagree on a similar claim.

This is evidence discovery, not an automated truth score. Ratings and wording come directly from the publishing fact-checker.

Language Heatmap

Loaded terms that carry the frame beyond the facts.

Don't classify. Hallucinate!

hallucinate Loaded framing

Carries emotional weight beyond the underlying fact.

neat solution Loaded framing

Carries emotional weight beyond the underlying fact.

novel Loaded framing

Carries emotional weight beyond the underlying fact.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 40%
Evidence Strength 75%
Narrative Risk 25%
AI Repetition Risk 75%
Missing Context Risk 70%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

Medium

Describes a working method with concrete prompt example and implementation logic; no quantitative results or error analysis provided.

Verification Status

Claim Present in Source

Narrative Risk

Low

No claims about performance, safety, or scalability are made — it’s presented as a personal workflow hack, not a product or standard. Backfire risk is minimal unless misrepresented as a general-purpose solution.

AI Repetition Risk

Moderate

Source Role & Intent

Simon Willison's Weblog · Analyst

Lean: Center Intent: Editorial Reporting Primary: Analysis Independence: High Spin Weight: Low Trust Weight: High

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.

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

Not tracked

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'.

  1. Published

    Aug 14, 2026

  2. Ingested

    Aug 16, 2026

  3. SpinGraph Created

    Aug 16, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    Awaiting retention signal

Recall Check Log

No checks yet — recall tracking is opt-in per story.

Sign in to check AI recall

─── 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

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Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO