自社のLLMにプロンプトを出すのではダメなのか?
It's the question every executive is being asked right now.
We're going to give you the honest answer.
The honest answer
If your question is “what will affluent customers in their forties do when interest rates rise?”, you can prompt your own LLM with a segment profile and get an answer.
The answer will be confident. It will be plausible. It will be cheap.
It will also be a hallucination dressed up as evidence, and the next person who reviews the decision it shaped will not be able to tell you why it said what it said.
Three approaches, one of them works
Digital twins, synthetic personas, and synthetic twins are not three names for the same thing. The category looks crowded. It isn’t.
Built from
Unit of analysis
Variance
Validation against ground truth
Auditability
86% vs 57% on the same task.
We ran a head-to-head: predict bank choice for a defined consumer segment, validated against real respondent panellists.
銀行選好の予測 · Brox vs LLM vs 汎用
実在する回答者パネリストに対して検証
Across other validated tasks (price sensitivity, concept testing, message resonance, switching intent, ad creative testing) the gap held. Real-human-twin panels beat LLM impersonation on every single one.
Why the gap exists
Three reasons.
LLMs are trained on what people wrote, not how they behave.
The world’s text is biased toward what people say in public. Most decisions are not made in public. Survey responses, interview transcripts, behavioural prompts and decision-trees, the raw material of how a real human actually decides, is not in the training set of any frontier model.
LLM-impersonation collapses the variance.
When you ask an LLM to “be” a 47-year-old nurse in Atlanta, you get the average of every 47-year-old nurse the model has ever read about. Real humans are weirder, sharper, more contradictory, more accurate. You need the variance, not the mean.
There is no audit trail.
An LLM cannot tell you why it said what it said, because no real person was ever consulted. With Brox, when a twin says something unexpected, you drop into the underlying interview transcript and see why.
An LLM cannot tell you why it said what it said.
A digital twin always can.
When an LLM is the right tool
We’re not anti-LLM. We use Claude every day. There are tasks where a prompted LLM is the best tool: drafting marketing copy, summarising a meeting, debugging code, brainstorming a campaign, generating creative variants for testing.
But for any decision where you need to predict what real humans will do, with traceability and an audit trail, drug launch, credit policy, ad spend, pricing, M&A diligence, crisis simulation, the LLM is the wrong instrument.
さらに詳しく
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