Run the same prompt several times per row

Calls the model .times times for every row of .data (all replicates run through the parallel engine in one pass) and appends one column per replicate: <output>_1, <output>_2, .... Feed the result to llm_agreement() for per-row majority labels and overall reliability.

Usage

llm_replicate(
  .data,
  output,
  prompt,
  .config,
  .times = 3L,
  .system_prompt = NULL,
  ...
)

Arguments

.data

A data.frame / tibble.

output

Unquoted base name for the replicate columns.

prompt

A glue template string evaluated against the columns of .data (as in llm_mutate()).

.config

An llm_config object (generative).

.times

Number of replicates (default 3).

.system_prompt

Optional system message.

...

Passed to call_llm_par() (e.g. tries, progress).

Value

.data with .times new character columns, <output>_1 ... <output>_<.times> (NA where a call failed).

Details

Replication only measures sampling variability if the model can vary: with temperature = 0 (or a fixed seed) most providers return nearly identical draws, which inflates agreement. Conversely, for measurement purposes you may want exactly that check: high disagreement at low temperature signals a prompt the model finds genuinely ambiguous.

See also

llm_agreement(), llm_mutate(), call_llm_par()

Examples

if (FALSE) { # \dontrun{
cfg <- llm_config("groq", "openai/gpt-oss-20b", temperature = 1)
df <- tibble::tibble(text = c("I loved it", "Meh", "Terrible service"))
reps <- df |>
  llm_replicate(sentiment,
                prompt = "Sentiment of '{text}'. One word: positive, negative, or neutral.",
                .config = cfg, .times = 5)
llm_agreement(reps, prefix = "sentiment")
} # }