# 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:

- `"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()