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
print(json.dumps(results
indent=2
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General
PromptBeginner5 minmarkdown
When humanity encounters aliens
the military asks: *Are you hostile?*
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General
PromptBeginner5 minmarkdown
prompt = render_prompt(chain[prompt]
context)
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General
PromptBeginner5 minmarkdown
output = self.llm(prompt
chain.get(output_schema))
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General
PromptBeginner5 minmarkdown
samples = stratified_sample(corpus
n=sample_size)
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General
PromptBeginner5 minmarkdown
def __init__(self
llm_fn=None):
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General
PromptBeginner5 minmarkdown
def run(self
corpus: list[dict]
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General
PromptBeginner5 minmarkdown
result = result.replace(placeholder
value_str)
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General
PromptBeginner5 minmarkdown
def call_llm(prompt: str
output_schema: dict = None) -> dict | str:
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General
PromptBeginner5 minmarkdown
return {_placeholder: True
schema: output_schema}
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General
PromptBeginner5 minmarkdown
for key
value in variables.items():
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General
PromptBeginner5 minmarkdown
if isinstance(value
(dict
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General
PromptBeginner5 minmarkdown
value_str = json.dumps(value
indent=2
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General
PromptBeginner5 minmarkdown
def render_prompt(template: str
variables: dict[str
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General
PromptBeginner5 minmarkdown
elif isinstance(sample
str):
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General
PromptBeginner5 minmarkdown
if isinstance(sample
dict):
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General
PromptBeginner5 minmarkdown
for turn in sample.get(turns
[]):
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General
PromptBeginner5 minmarkdown
role = turn.get(role
?)
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General
PromptBeginner5 minmarkdown
def stratified_sample(corpus: list[dict]
n: int = 15) -> list[dict]:
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General
PromptBeginner5 minmarkdown
# Simple stratified: divide into chunks
sample from each
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General
PromptBeginner5 minmarkdown
for i
sample in enumerate(samples
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General
PromptBeginner5 minmarkdown
def load_all_chains() -> dict[str
dict]:
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