Small experiment with LLMR
In LLMR: Interface for Large Language Model APIs in R

knitr::opts_chunk$set(
  collapse = TRUE, comment = "#>",
  eval = identical(tolower(Sys.getenv("LLMR_RUN_VIGNETTES", "false")), "true") )

We will compare three configurations on two prompts, once unstructured and once with structured output. In choosing models, note that at the time of writing this vignette, Gemini models are not guaranteeing the schema output and is more likely to run into trouble.

library(LLMR)
library(dplyr)
cfg_openai <- llm_config("openai",   "gpt-5-nano")
cfg_cld    <- llm_config("anthropic","claude-sonnet-4-20250514", max_tokens = 512)
cfg_gem    <- llm_config("groq",     "openai/gpt-oss-20b")

Build a factorial design

experiments <- build_factorial_experiments(
  configs       = list(cfg_openai, cfg_cld, cfg_gem),
  user_prompts  = c("Summarize in one sentence: The Apollo program.",
                    "List two benefits of green tea."),
  system_prompts = c("Be concise.")
)
experiments

Run unstructured

setup_llm_parallel(workers = 10)
res_unstructured <- call_llm_par(experiments, progress = TRUE)
reset_llm_parallel()
res_unstructured |>
  select(provider, model, user_prompt_label, response_text, finish_reason) |>
  head()

Structured version

schema <- list(
  type = "object",
  properties = list(
    answer = list(type="string"),
    keywords = list(type="array", items = list(type="string"))
  ),
  required = list("answer","keywords"),
  additionalProperties = FALSE
)

experiments2 <- experiments
experiments2$config <- lapply(experiments2$config, enable_structured_output, schema = schema)

setup_llm_parallel(workers = 10)
res_structured <- call_llm_par_structured(experiments2 , .fields = c("answer","keywords") )
reset_llm_parallel()

res_structured |>
  select(provider, model, user_prompt_label, structured_ok, answer) |>
  head()