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Small experiment with LLMR

Small experiment with LLMR

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

Overview

This vignette demonstrates:

  1. Building factorial experiment designs with build_factorial_experiments()
  2. Running experiments in parallel with call_llm_par()
  3. Comparing unstructured vs. structured output across providers

The workflow builds a factorial design, runs the calls in parallel, and then analyzes the returned tibble.

We will compare three configurations on two prompts, once unstructured and once with structured output. We use open models via DeepSeek and Groq to ensure fast and low-cost execution.

library(LLMR)
library(dplyr)
cfg_ds     <- llm_config("deepseek", "deepseek-chat")
cfg_groq1  <- llm_config("groq",     "llama-3.1-8b-instant")
cfg_groq   <- llm_config("groq",     "openai/gpt-oss-20b")

Build a factorial design

experiments <- build_factorial_experiments(
  configs       = list(cfg_ds, cfg_groq1, cfg_groq),
  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()

Understanding the results:

The finish_reason column shows why each response ended:

The user_prompt_label helps track which experimental condition produced each response.

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()

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