# Small experiment with LLMR

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

## Overview

Comparative studies ask whether responses change across models, conditions, and repeated draws. This example covers three model configurations and two task conditions, runs the factorial design in parallel, and compares unstructured with schema-structured results.

For broader designs, `repetitions`, `call_llm_compare()`, `call_llm_sweep()`, `llm_par_resume()`, `llm_failures()`, and `llm_log_enable()` support repeated runs, direct comparisons, parameter sweeps, interrupted-run recovery, diagnostics, and call logging. Those facilities are outside this example. Live execution requires `DEEPSEEK_API_KEY`, `GROQ_API_KEY`, and `LLMR_RUN_VIGNETTES=true` in the R environment.

    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:

- `"stop"`: normal completion
- `"length"`: hit token limit (increase `max_tokens`)
- `"filter"`: content filter triggered

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()