diff --git a/DESCRIPTION b/DESCRIPTION
index fded77f48..bbc018a48 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.2.1.9003
+Version: 1.2.1.9004
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
diff --git a/NAMESPACE b/NAMESPACE
index 0782892d6..ea8dce360 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -185,6 +185,7 @@ export(.dat)
 export(.extract_surv_status)
 export(.extract_surv_time)
 export(.facts)
+export(.get_prediction_column_names)
 export(.lvls)
 export(.model_param_name_key)
 export(.obs)
diff --git a/R/misc.R b/R/misc.R
index 3582d8ca2..263a9b0f2 100644
--- a/R/misc.R
+++ b/R/misc.R
@@ -575,3 +575,75 @@ is_cran_check <- function() {
 }
 # nocov end
 
+# ------------------------------------------------------------------------------
+
+#' Obtain names of prediction columns for a fitted model or workflow
+#'
+#' [.get_prediction_column_names()] returns a list that has the names of the
+#' columns for the primary prediction types for a model.
+#' @param x A fitted parsnip model (class `"model_fit"`) or a fitted workflow.
+#' @param syms Should the column names be converted to symbols? Defaults to `FALSE`.
+#' @return A list with elements `"estimate"` and `"probabilities"`.
+#' @examplesIf !parsnip:::is_cran_check()
+#' library(dplyr)
+#' library(modeldata)
+#' data("two_class_dat")
+#'
+#' levels(two_class_dat$Class)
+#' lr_fit <- logistic_reg() %>% fit(Class ~ ., data = two_class_dat)
+#'
+#' .get_prediction_column_names(lr_fit)
+#' .get_prediction_column_names(lr_fit, syms = TRUE)
+#' @export
+.get_prediction_column_names <- function(x, syms = FALSE) {
+  if (!inherits(x, c("model_fit", "workflow"))) {
+    cli::cli_abort("{.arg x} should be an object with class {.cls model_fit} or
+                    {.cls workflow}, not {.obj_type_friendly {x}}.")
+  }
+
+  if (inherits(x, "workflow")) {
+    x <- x %>% extract_fit_parsnip(x)
+  }
+  model_spec <- extract_spec_parsnip(x)
+  model_engine <- model_spec$engine
+  model_mode <- model_spec$mode
+  model_type <- class(model_spec)[1]
+
+  # appropriate populate the model db
+  inst_res <- purrr::map(required_pkgs(x), rlang::check_installed)
+  predict_types <-
+    get_from_env(paste0(model_type, "_predict")) %>%
+    dplyr::filter(engine == model_engine & mode == model_mode) %>%
+    purrr::pluck("type")
+
+  if (length(predict_types) == 0) {
+    cli::cli_abort("Prediction information could not be found for this
+                   {.fn {model_type}} with engine {.val {model_engine}} and mode
+                   {.val {model_mode}}. Does a parsnip extension package need to
+                   be loaded?")
+  }
+
+  res <- list(estimate = character(0), probabilities = character(0))
+
+  if (model_mode == "regression") {
+    res$estimate <- ".pred"
+  } else if (model_mode == "classification") {
+    res$estimate <- ".pred_class"
+    if (any(predict_types == "prob")) {
+      res$probabilities <- paste0(".pred_", x$lvl)
+    }
+  } else if (model_mode == "censored regression") {
+    res$estimate <- ".pred_time"
+    if (any(predict_types %in% c("survival"))) {
+      res$probabilities <- ".pred"
+    }
+  } else {
+    # Should be unreachable
+    cli::cli_abort("Unsupported model mode {model_mode}.")
+  }
+
+  if (syms) {
+    res <- purrr::map(res, rlang::syms)
+  }
+  res
+}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index c79ecca06..3596da3f3 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -111,3 +111,4 @@ reference:
       - .extract_surv_status
       - .extract_surv_time
       - .model_param_name_key
+      - .get_prediction_column_names
diff --git a/man/dot-get_prediction_column_names.Rd b/man/dot-get_prediction_column_names.Rd
new file mode 100644
index 000000000..36fba2c08
--- /dev/null
+++ b/man/dot-get_prediction_column_names.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/misc.R
+\name{.get_prediction_column_names}
+\alias{.get_prediction_column_names}
+\title{Obtain names of prediction columns for a fitted model or workflow}
+\usage{
+.get_prediction_column_names(x, syms = FALSE)
+}
+\arguments{
+\item{x}{A fitted model (class \code{"model_fit"}) or a fitted workflow.}
+
+\item{syms}{Should the column names be converted to symbols?}
+}
+\value{
+A list with elements \code{"estimate"} and \code{"probabilities"}.
+}
+\description{
+\code{\link[=.get_prediction_column_names]{.get_prediction_column_names()}} returns a list that has the names of the
+columns for the primary prediction types for a model.
+}
+\examples{
+\dontshow{if (!parsnip:::is_cran_check()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+library(dplyr)
+library(modeldata)
+data("two_class_dat")
+
+levels(two_class_dat$Class)
+lr_fit <- logistic_reg() \%>\% fit(Class ~ ., data = two_class_dat)
+
+.get_prediction_column_names(lr_fit)
+.get_prediction_column_names(lr_fit, syms = TRUE)
+\dontshow{\}) # examplesIf}
+}
diff --git a/tests/testthat/_snaps/misc.md b/tests/testthat/_snaps/misc.md
index b6b1f918c..b221b1dde 100644
--- a/tests/testthat/_snaps/misc.md
+++ b/tests/testthat/_snaps/misc.md
@@ -227,3 +227,19 @@
       Error in `check_outcome()`:
       ! For a censored regression model, the outcome should be a <Surv> object, not an integer vector.
 
+# obtaining prediction columns
+
+    Code
+      .get_prediction_column_names(1)
+    Condition
+      Error in `.get_prediction_column_names()`:
+      ! `x` should be an object with class <model_fit> or <workflow>, not a number.
+
+---
+
+    Code
+      .get_prediction_column_names(unk_fit)
+    Condition
+      Error in `.get_prediction_column_names()`:
+      ! Prediction information could not be found for this `linear_reg()` with engine "lm" and mode "Depeche". Does a parsnip extension package need to be loaded?
+
diff --git a/tests/testthat/test-misc.R b/tests/testthat/test-misc.R
index e689bcbcf..901c92748 100644
--- a/tests/testthat/test-misc.R
+++ b/tests/testthat/test-misc.R
@@ -249,3 +249,53 @@ test_that('check_outcome works as expected', {
     check_outcome(1:2, cens_spec)
   )
 })
+
+# ------------------------------------------------------------------------------
+
+test_that('obtaining prediction columns', {
+  skip_if_not_installed("modeldata")
+  data(two_class_dat, package = "modeldata")
+
+  ### classification
+  lr_fit <- logistic_reg() %>% fit(Class ~ ., data = two_class_dat)
+  expect_equal(
+    .get_prediction_column_names(lr_fit),
+    list(estimate = ".pred_class",
+         probabilities = c(".pred_Class1", ".pred_Class2"))
+  )
+  expect_equal(
+    .get_prediction_column_names(lr_fit, syms = TRUE),
+    list(estimate = list(quote(.pred_class)),
+         probabilities = list(quote(.pred_Class1), quote(.pred_Class2)))
+  )
+
+  ### regression
+  ols_fit <- linear_reg() %>% fit(mpg ~ ., data = mtcars)
+  expect_equal(
+    .get_prediction_column_names(ols_fit),
+    list(estimate = ".pred",
+         probabilities = character(0))
+  )
+  expect_equal(
+    .get_prediction_column_names(ols_fit, syms = TRUE),
+    list(estimate = list(quote(.pred)),
+         probabilities = list())
+  )
+
+  ### censored regression
+  # in extratests
+
+  ### bad input
+  expect_snapshot(
+    .get_prediction_column_names(1),
+    error = TRUE
+  )
+
+  unk_fit <- ols_fit
+  unk_fit$spec$mode <- "Depeche"
+  expect_snapshot(
+    .get_prediction_column_names(unk_fit),
+    error = TRUE
+  )
+
+})