Find agent skills by outcome

Terapide dönüştürücü sayılan soru

teknik destekte dönüştürücü sayılandan farklıdır. Bir bağlamda derinlik açan soru

Geleneksel yaklaşımlar sabit metrikler kullanır: duygu skorları

etkileşim oranları

LLMs are increasingly used to generate questions: in therapy bots

coaching apps

These require judgment

empathy

During testing

the final Socratic chain asked:

- Human-final: shows candidates

human decides

| Elenchus (examination) | Chain 5: Apply lens

find tensions |

| Human as final authority | System shows

human decides |

- A mirror

not a judge

- Socratic: it asks

it doesn't conclude

- A discovery method

not a scoring algorithm

- Introduces value trade-offs (money vs belonging

loss vs gain)

Data

In a philosophical dataset

context might mean abstraction level.

toward examining internal meaning

assumptions

Data

In a therapy dataset

context might mean emotional depth.

Louise

the linguist

Data

In a technical dataset

context might mean problem scope.

Traditional approaches use fixed metrics: sentiment scores

engagement rates

print(json.dumps(results

indent=2

When humanity encounters aliens

the military asks: *Are you hostile?*

prompt = render_prompt(chain[prompt]

context)

output = self.llm(prompt

chain.get(output_schema))

samples = stratified_sample(corpus

n=sample_size)

def init(self

llm_fn=None):