Constructs the agents, moderator guide, and turn-taking flow, then runs one focus group session. The model configuration is explicit. Before a live run, the function estimates the number of generated model outputs and applies the `max_calls` limit unless `confirm` is `TRUE`.
Usage
run_focus_group(
topic,
config,
n_participants = 6,
guide = c(Opening = 2, Icebreaker = 3, Engagement = 8, Exploration = 10, Closing = 2),
demographics = NULL,
survey_responses = NULL,
flow = "desire_based",
seed = NULL,
message_mode = c("roleflip", "flat"),
verbose = TRUE,
max_participant_responses = NULL,
max_calls = 100L,
confirm = FALSE,
.runner = NULL
)Arguments
- topic
Character. Focus group topic.
- config
An explicit `LLMR::llm_config` used by all agents and group-level model tasks.
- n_participants
Integer. Number of participants, excluding the moderator.
- guide
A named numeric vector or named list. Numeric values select that many moderator instructions from the phase banks. Character vectors supply the ordered instructions directly.
- demographics
Optional participant demographics.
- survey_responses
Optional participant survey responses.
- flow
Character. One of `"round_robin"`, `"probabilistic"`, or `"desire_based"`.
- seed
Optional integer governing in-package sampling.
- message_mode
Character. `"roleflip"` or `"flat"`.
- verbose
Logical. Print session progress.
- max_participant_responses
Optional integer maximum number of participant responses per moderator question.
- max_calls
Integer. Maximum estimated live model outputs allowed without confirmation.
- confirm
Logical. Permit a live run whose estimate exceeds `max_calls`.
- .runner
Optional function used instead of live model calls. It receives a data frame with `config` and `messages` list-columns and returns those rows with at least `response_text`.
Value
A `focus_group_result` with the group, transcript, summary, participant table, token usage, and sanitized metadata.
Examples
if (FALSE) { # \dontrun{
cfg <- LLMR::llm_config("openai", "gpt-4o-mini")
result <- run_focus_group(
topic = "Library funding priorities",
config = cfg,
n_participants = 4,
guide = list(
Opening = "Welcome the participants and state the ground rules.",
Exploration = "Which funding priority deserves attention first?",
Closing = "Thank the participants and close the session."
),
flow = "round_robin"
)
} # }