Creative
PromptBeginner5 minmarkdown
Bu sistem
*Arrival* (2016) filmindeki bir gözlemle başladı:
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General
PromptBeginner5 minmarkdown
During testing
the final Socratic chain asked:
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General
PromptBeginner5 minmarkdown
Geleneksel yaklaşımlar sabit metrikler kullanır: duygu skorları
etkileşim oranları
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General
PromptBeginner5 minmarkdown
LLMs are increasingly used to generate questions: in therapy bots
coaching apps
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General
PromptBeginner5 minmarkdown
| Human as final authority | System shows
human decides |
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General
PromptBeginner5 minmarkdown
These require judgment
empathy
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General
PromptBeginner5 minmarkdown
| Elenchus (examination) | Chain 5: Apply lens
find tensions |
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General
PromptBeginner5 minmarkdown
- **Socratic**: it asks
it doesn't conclude
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General
PromptBeginner5 minmarkdown
- **Human-final**: shows candidates
human decides
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General
PromptBeginner5 minmarkdown
- A **mirror**
not a judge
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General
PromptBeginner5 minmarkdown
- A **discovery method**
not a scoring algorithm
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General
PromptBeginner5 minmarkdown
toward examining internal meaning
assumptions
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General
PromptBeginner5 minmarkdown
- Introduces value trade-offs (money vs belonging
loss vs gain)
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Data
PromptBeginner5 minmarkdown
In a therapy dataset
context might mean emotional depth.
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Data
PromptBeginner5 minmarkdown
In a philosophical dataset
context might mean abstraction level.
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Data
PromptBeginner5 minmarkdown
In a technical dataset
context might mean problem scope.
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General
PromptBeginner5 minmarkdown
Traditional approaches use fixed metrics: sentiment scores
engagement rates
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General
PromptBeginner5 minmarkdown
Louise
the linguist
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General
PromptBeginner5 minmarkdown
When humanity encounters aliens
the military asks: *Are you hostile?*
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General
PromptBeginner5 minmarkdown
print(json.dumps(results
indent=2
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General
PromptBeginner5 minmarkdown
output = self.llm(prompt
chain.get(output_schema))
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General
PromptBeginner5 minmarkdown
def run(self
corpus: list[dict]
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General
PromptBeginner5 minmarkdown
prompt = render_prompt(chain[prompt]
context)
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General
PromptBeginner5 minmarkdown
samples = stratified_sample(corpus
n=sample_size)
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