diff --git a/DESCRIPTION b/DESCRIPTION
index 42d3e44..c3fc7d3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -4,18 +4,17 @@ Title: Batch Process LLM Text Completions Using a Data Frame
 Version: 0.1.0
 Authors@R: person("Dylan", "Pieper", email = "dylanpieper@gmail.com", role = c("aut", "cre", "cph"))
 Maintainer: Dylan Pieper <dylanpieper@gmail.com>
-Description: Batch process large language model (LLM) text completions using data frame rows, with support for OpenAI's GPT, Anthropic's Claude, and Google's Gemini. Includes features such as local storage, metadata logging, API rate limiting delays, and a Shiny App Addin.
+Description: Batch process large language model (LLM) text completions using data frame rows, with support for OpenAI's 'GPT' (<https://chat.openai.com>), Anthropic's 'Claude' (<https://claude.ai>), and Google's 'Gemini' (<https://gemini.google.com>). Includes features such as local storage, metadata logging, API rate limiting delays, and a 'shiny' app addin.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 URL: https://github.com/dylanpieper/batchLLM
 BugReports: https://github.com/dylanpieper/batchLLM/issues
 Imports:
     openai,
     gemini.R,
     rlang,
-    utils,
     stats,
     digest,
     dplyr,
diff --git a/NAMESPACE b/NAMESPACE
index f686a2c..bb75260 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -94,5 +94,3 @@ importFrom(stats,runif)
 importFrom(stats,setNames)
 importFrom(tools,file_ext)
 importFrom(tools,file_path_sans_ext)
-importFrom(utils,setTxtProgressBar)
-importFrom(utils,txtProgressBar)
diff --git a/R/batchLLM.R b/R/batchLLM.R
index a5c6195..29c2e50 100644
--- a/R/batchLLM.R
+++ b/R/batchLLM.R
@@ -15,18 +15,17 @@
 #' @param max_tokens A maximum number of tokens to generate before stopping. Default is 500.
 #' @param batch_delay A string for the batch delay with the options: "random", "min", and "sec". Numeric examples include "1min" and "30sec". Default is "random" which is an average of 10.86 seconds (n = 1,000 simulations).
 #' @param batch_size The number of rows to process in each batch. Default is 10.
-#' @param extract_XML Extract the LLM text completion from the model's response by returning only content in \code{<result>} XML tags. This helps prevent unwanted text (e.g., preamble) from being included in the model's output. Default is TRUE.
+#' @param case_convert A string for the case conversion of the output with the options: "upper", "lower", or NULL (no change). Default is NULL.
+#' @param sanitize Extract the LLM text completion from the model's response by returning only content in \code{<result>} XML tags. Additionally, remove all punctuation. This feature prevents unwanted text (e.g., preamble) or punctuation from being included in the model's output. Default is FALSE.
 #' @param attempts The maximum number of loop retry attempts. Default is 1.
 #' @param log_name A string for the name of the log without the \code{.rds} file extension. Default is "batchLLM-log".
 #' @param hash_algo A string for a hashing algorithm from the 'digest' package. Default is \code{crc32c}.
-#' @param case_convert A string for the case conversion of the output with the options: "upper", "lower", or NULL (no change). Default is NULL.
 #' @param ... Additional arguments to pass on to the LLM API function.
 #' @return
 #' Returns the input data frame with an additional column containing the text completion output.
 #' The function also writes the output and metadata to the log file after each batch in a nested list format.
 #' @importFrom openai create_chat_completion
 #' @importFrom gemini.R gemini_chat
-#' @importFrom utils txtProgressBar setTxtProgressBar
 #' @importFrom stats runif
 #' @importFrom digest digest
 #' @importFrom dplyr mutate left_join row_number rename_with ends_with
@@ -76,11 +75,11 @@ batchLLM <- function(df,
                      max_tokens = 500,
                      batch_delay = "random",
                      batch_size = 10,
-                     extract_XML = FALSE,
+                     case_convert = NULL,
+                     sanitize = FALSE,
                      attempts = 1,
                      log_name = "batchLLM-log",
                      hash_algo = "crc32c",
-                     case_convert = NULL,
                      ...) {
   df_string <- if (!is.null(df_name)) df_name else deparse(substitute(df))
   col <- rlang::enquo(col)
@@ -136,7 +135,7 @@ batchLLM <- function(df,
     }
   }
 
-  extract_xml <- function(content, tag = "results") {
+  sanitize_output <- function(content, tag = "results") {
     if (is.null(content) || is.na(content) || !nzchar(content)) {
       return(NA_character_)
     }
@@ -144,10 +143,11 @@ batchLLM <- function(df,
     if (result == content) {
       return(NA_character_)
     }
+    result <- gsub("[[:punct:]]", "", result)
     return(result)
   }
 
-  sanitize_output <- function(content, case_convert) {
+  case_convert_output <- function(content, case_convert) {
     if (!is.null(case_convert) && case_convert != "none") {
       if (case_convert == "upper") {
         content <- toupper(content)
@@ -158,20 +158,20 @@ batchLLM <- function(df,
     return(trimws(content))
   }
 
-  batch_mutate <- function(df, df_col, df_string, col_string, system_prompt, batch_size, batch_delay, batch_num, batch_total, LLM, model, temperature, start_row, total_rows, log_name, case_convert, max_tokens, extract_XML, ...) {
-    build_prompt <- function(system_prompt, content_input, extract_XML) {
-      if (extract_XML) {
+  batch_mutate <- function(df, df_col, df_string, col_string, system_prompt, batch_size, batch_delay, batch_num, batch_total, LLM, model, temperature, start_row, total_rows, log_name, case_convert, sanitize, max_tokens, ...) {
+    build_prompt <- function(system_prompt, content_input, sanitize) {
+      if (sanitize) {
         paste0(system_prompt, "(put your response in a single level of XML tags <results></results> and continue in plain text):", as.character(content_input))
       } else {
         paste0(system_prompt, ":", as.character(content_input))
       }
     }
 
-    mutate_row <- function(df_string, df_row, content_input, system_prompt, LLM, model, temperature, batch_delay, log_name, case_convert, max_tokens, extract_XML, ...) {
+    mutate_row <- function(df_string, df_row, content_input, system_prompt, LLM, model, temperature, batch_delay, log_name, case_convert, sanitize, max_tokens, ...) {
       if (length(content_input) == 1 && !is.na(content_input)) {
         tryCatch(
           {
-            prompt <- build_prompt(system_prompt, content_input, extract_XML)
+            prompt <- build_prompt(system_prompt, content_input, sanitize)
             content_output <- NULL
 
             if (grepl("openai", LLM)) {
@@ -185,8 +185,8 @@ batchLLM <- function(df,
                 ),
                 ...
               )
-              if (extract_XML) {
-                content_output <- extract_xml(completion$choices$message.content)
+              if (sanitize) {
+                content_output <- sanitize_output(completion$choices$message.content)
               } else {
                 content_output <- completion$choices$message.content
               }
@@ -202,8 +202,8 @@ batchLLM <- function(df,
                 max_tokens = max_tokens,
                 ...
               )
-              if (extract_XML) {
-                content_output <- extract_xml(completion)
+              if (sanitize) {
+                content_output <- sanitize_output(completion)
               } else {
                 content_output <- completion
               }
@@ -215,8 +215,8 @@ batchLLM <- function(df,
                 maxOutputTokens = max_tokens,
                 ...
               )
-              if (extract_XML) {
-                content_output <- extract_xml(completion$outputs[["text"]])
+              if (sanitize) {
+                content_output <- sanitize_output(completion$outputs[["text"]])
               } else {
                 content_output <- completion$outputs[["text"]]
               }
@@ -224,23 +224,24 @@ batchLLM <- function(df,
 
             if (is.null(content_output)) stop("Failed to obtain content_output.")
             if (length(content_output) > 0) {
-              output_text <- sanitize_output(content_output[1], case_convert)
+              output_text <- case_convert_output(content_output[1], case_convert)
               return(output_text)
             } else {
-              stop("Error: completion message content returned NULL or empty")
+              stop("\U0001F6D1 Error: completion message content returned NULL or empty")
             }
           },
           error = function(e) {
-            stop(paste("Error occurred in mutate_row:", conditionMessage(e)))
+            stop(paste("\U0001F6D1 Error occurred in mutate_row:", conditionMessage(e)))
           }
         )
       } else {
-        stop("Invalid input: content_input must be a single non-NA value")
+        stop("\U0001F6D1 Invalid input: content_input must be a single non-NA value")
       }
     }
 
     df <- df |> dplyr::mutate(row_number = dplyr::row_number() + start_row - 1)
     result <- vector("character", nrow(df))
+
     for (i in seq_len(nrow(df))) {
       if (!is.na(df_col[i])) {
         result[i] <- tryCatch(
@@ -256,26 +257,28 @@ batchLLM <- function(df,
               batch_delay = batch_delay,
               log_name = log_name,
               case_convert = case_convert,
-              max_tokens = max_tokens,
-              extract_XML = extract_XML
+              sanitize = sanitize,
+              max_tokens = max_tokens
             )
           },
           error = function(e) {
-            stop(paste("Error in batch_mutate:", conditionMessage(e)))
+            stop(paste("\U0001F6D1 Error in batch_mutate:", conditionMessage(e)))
           }
         )
-        message(paste("Processed row", df$row_number[i], "of", total_rows))
+        message(paste("\u27A4 Processed row", df$row_number[i], "of", total_rows))
       } else {
-        message(paste("Skipping row", df$row_number[i], "(NA value)"))
+        message(paste("\u26A0\uFE0F Skipping row", df$row_number[i], "(NA value)"))
       }
       Sys.sleep(runif(1, min = 0.1, max = 0.3))
     }
+
     if (batch_num < batch_total) {
-      message("Taking a break to make the API happy \U0001F916\U00002764")
-      pb <- txtProgressBar(min = 0, max = 100, style = 3)
+      message("\u231B\uFE0F Taking a break to make the API happy")
+
       matches <- regmatches(batch_delay, regexec("(\\d+)(\\w+)", batch_delay))[[1]]
       delay_value <- as.numeric(matches[2])
       delay_unit <- matches[3]
+
       if (grepl("sec", delay_unit)) {
         delay <- delay_value / 100
       } else if (grepl("min", delay_unit)) {
@@ -283,30 +286,28 @@ batchLLM <- function(df,
       } else if (grepl("random", batch_delay)) {
         delay <- sample(seq(0.05, 0.15, by = 0.01), 1)
       } else {
-        cat("\n")
-        stop("Invalid unit of time.")
+        stop("\U0001F6D1 Invalid unit of time.")
       }
+
       for (i in 1:100) {
-        Sys.sleep(delay)
-        setTxtProgressBar(pb, i)
-        if (i %in% c(25, 50, 75)) {
-          cat("\n")
-          message(i, "% through the break")
+        Sys.sleep(delay / 100)
+        if (i %in% c(25, 50, 75, 100)) {
+          message(paste0("\U0001F4A4 ", i, "% through the break"))
         }
       }
-      close(pb)
+
     }
     df$llm_output <- result
     return(df)
   }
 
   if (!is.data.frame(df) || !inherits(df, "data.frame")) {
-    stop("Input must be a valid data frame.")
+    stop("\U0001F6D1 Input must be a valid data frame.")
   }
   if (!col_string %in% colnames(df)) {
-    stop(paste("Column", col_string, "does not exist in the input data frame."))
+    stop(paste("\U0001F6D1 Column", col_string, "does not exist in the input data frame."))
   }
-  param_id <- digest::digest(list(LLM, model, temperature, prompt, batch_size, batch_delay, max_tokens, extract_XML), algo = hash_algo)
+  param_id <- digest::digest(list(LLM, model, temperature, prompt, batch_size, batch_delay, max_tokens, sanitize), algo = hash_algo)
   new_col <- paste0(col_string, "_", param_id, algo = hash_algo)
   param_id <- digest::digest(df[[col_string]], algo = hash_algo)
   new_df_key <- paste0(df_string, "_", param_id)
@@ -326,12 +327,12 @@ batchLLM <- function(df,
         0
       }
       if (nrow(output) == nrow(df) && !any(is.na(output[[new_col]]))) {
-        message("All rows have already been processed for this column using the current config.")
+        message("\u2714 All rows have already been processed for this column using the current config.")
         df[[new_col]] <- output[[new_col]]
         return(df)
       }
       if (last_batch > 0) {
-        message("Resuming from batch ", last_batch + 1)
+        message("\U0001F6A9 Resuming from batch ", last_batch + 1)
       }
     } else {
       output <- if (is.null(progress$output)) df else progress$output
@@ -348,12 +349,12 @@ batchLLM <- function(df,
   start_time <- Sys.time()
   batch_total <- ceiling(nrow(df) / batch_size)
   for (batch_num in (last_batch + 1):batch_total) {
-    message("Starting batch ", batch_num, " of ", batch_total)
+    message("\U0001F6A9 Starting batch ", batch_num, " of ", batch_total)
     start_row <- (batch_num - 1) * batch_size + 1
     end_row <- min(batch_num * batch_size, nrow(df))
     rows_to_process <- which(is.na(output[start_row:end_row, new_col])) + start_row - 1
     if (length(rows_to_process) == 0) {
-      message("Skipping batch ", batch_num, " of ", batch_total, " as all rows are already processed.")
+      message("\u26A0\uFE0F Skipping batch ", batch_num, " of ", batch_total, " as all rows are already processed.")
       last_batch <- batch_num
       next
     }
@@ -393,8 +394,8 @@ batchLLM <- function(df,
             total_rows = nrow(df),
             log_name = log_name,
             case_convert = case_convert,
-            max_tokens = max_tokens,
-            extract_XML = extract_XML
+            sanitize = sanitize,
+            max_tokens = max_tokens
           )
           output[rows_to_process, new_col] <- output_batch$llm_output
           current_time <- Sys.time()
@@ -415,7 +416,7 @@ batchLLM <- function(df,
             status = "Completed",
             log_name = log_name
           )
-          message("Completed batch ", batch_num, " of ", batch_total)
+          message("\U0001F6A9 Completed batch ", batch_num, " of ", batch_total)
           retry_flag <- FALSE
         },
         error = function(e) {
@@ -434,9 +435,9 @@ batchLLM <- function(df,
               status = "Interrupted",
               log_name = log_name
             )
-            stop(paste("Error in batch ", batch_num, " of ", batch_total, ": ", conditionMessage(e), sep = ""))
+            stop(paste("\U0001F6D1 Error in batch ", batch_num, " of ", batch_total, ": ", conditionMessage(e), sep = ""))
           } else {
-            message(paste("Error occurred in batch ", batch_num, " of ", batch_total, ", trying again. Data processed up to row ", max(rows_to_process), ": ", conditionMessage(e), ". Attempt: ", counter, sep = ""))
+            message(paste("\U0001F6D1 Error occurred in batch ", batch_num, " of ", batch_total, ", trying again. Data processed up to row ", max(rows_to_process), ": ", conditionMessage(e), ". Attempt: ", counter, sep = ""))
             counter <- counter + 1
             Sys.sleep(runif(1, min = 1, max = 2))
           }
@@ -477,20 +478,20 @@ get_batches <- function(df_name = NULL, log_name = "batchLLM-log") {
   log_file <- paste0(log_name, ".rds")
 
   if (!file.exists(log_file)) {
-    stop("Log file does not exist.")
+    stop("\u26A0\uFE0F Log file does not exist.")
   }
 
   batch_log <- readRDS(log_file)
 
   if (is.null(df_name)) {
     stop(paste(
-      "You need to define 'df_name' with one of the following names:",
+      "\U0001F6D1 Please define 'df_name' with a valid name:",
       paste(unique(scrape_metadata()$df), collapse = ", ")
     ))
   }
 
   if (!df_name %in% names(batch_log$data)) {
-    stop(paste0("No data found in ", log_name, ".rds for the specified df_name"))
+    stop(paste0("\U0001F6D1 No data found in ", log_name, ".rds for the specified df_name"))
   }
 
   output <- batch_log$data[[df_name]]$output
@@ -525,7 +526,7 @@ scrape_metadata <- function(df_name = NULL, log_name = "batchLLM-log") {
   log_file <- paste0(log_name, ".rds")
 
   if (!file.exists(log_file)) {
-    warning("Log file does not exist.")
+    warning("\u26A0\uFE0F Log file does not exist.")
     return(data.frame(
       df_name = character(),
       col = character(),
@@ -574,7 +575,7 @@ scrape_metadata <- function(df_name = NULL, log_name = "batchLLM-log") {
 
   if (!is.null(df_name)) {
     if (!df_name %in% names(batch_log$data)) {
-      message(paste("No data found for", df_name, "in the log file."))
+      message(paste("\u26A0\uFE0F No data found for", df_name, "in the log file."))
       return(NULL)
     }
     return(scrape_df(df_name, batch_log$data[[df_name]]))
@@ -653,13 +654,13 @@ claudeR <- function(
     api_key = NULL,
     system_prompt = NULL) {
   if (grepl("claude-3", model) && !is.list(prompt)) {
-    stop("Claude-3 requires the input in a list format, e.g., list(list(role = \"user\", content = \"What is the capital of France?\"))")
+    stop("\U0001F6D1 Claude-3 requires the input in a list format, e.g., list(list(role = \"user\", content = \"What is the capital of France?\"))")
   }
 
   if (is.null(api_key)) {
     api_key <- Sys.getenv("ANTHROPIC_API_KEY")
     if (api_key == "") {
-      stop("Please provide an API key or set it as the ANTHROPIC_API_KEY environment variable.")
+      stop("\U0001F6D1 Please provide an API key or set it as the ANTHROPIC_API_KEY environment variable.")
     }
   }
 
@@ -691,10 +692,8 @@ claudeR <- function(
       result <- jsonlite::fromJSON(httr::content(response, "text", encoding = "UTF-8"))
       return(trimws(result$completion))
     } else {
-      warning(paste("API request failed with status", httr::http_status(response)$message))
-      cat(api_key)
-      cat("Error details:\n", httr::content(response, "text", encoding = "UTF-8"), "\n")
-      return(NULL)
+      warning(paste("\U0001F6D1 API request failed with status", httr::http_status(response)$message))
+      stop("\U0001F6D1 Error details:\n", httr::content(response, "text", encoding = "UTF-8"), "\n")
     }
   }
 
@@ -731,8 +730,7 @@ claudeR <- function(
     result <- jsonlite::fromJSON(httr::content(response, "text", encoding = "UTF-8"))
     return(result$content$text)
   } else {
-    warning(paste("API request failed with status", httr::http_status(response)$message))
-    cat("Error details:\n", httr::content(response, "text", encoding = "UTF-8"), "\n")
-    return(NULL)
+    warning(paste("\U0001F6D1 API request failed with status", httr::http_status(response)$message))
+    stop("\U0001F6D1 Error details:\n", httr::content(response, "text", encoding = "UTF-8"), "\n")
   }
 }
diff --git a/R/batchLLM_shiny.R b/R/batchLLM_shiny.R
index 2eeece3..23f7e6c 100644
--- a/R/batchLLM_shiny.R
+++ b/R/batchLLM_shiny.R
@@ -3,6 +3,8 @@
 #' @description This function provides a user interface using Shiny to interact with
 #' the `batchLLM` package. It allows users to configure and execute batch processing
 #' through an interactive dashboard.
+#' 
+#' @return No return value. Launches a Shiny Gadget that allows users to interact with the `batchLLM` package.
 #'
 #' @importFrom shiny fluidPage fluidRow column titlePanel tabPanel tabsetPanel
 #' @importFrom shiny conditionalPanel HTML sidebarLayout sidebarPanel mainPanel
@@ -190,8 +192,8 @@ batchLLM_shiny <- function() {
                   justified = TRUE
                 ),
                 radioGroupButtons(
-                  inputId = "extract_XML",
-                  label = labelWithInfo("XML Extract:", "Requests a response in XML tags and removes unwanted text and preamble. May produce an unintended XML structure in longer responses."),
+                  inputId = "sanitize",
+                  label = labelWithInfo("Sanitize Output:", "Requests a response in XML tags and removes unwanted text (e.g., preamble) and all punctuation. May produce an unintended XML structure in longer responses."),
                   choices = c("False" = "FALSE", "True" = "TRUE"),
                   selected = "FALSE",
                   justified = TRUE
@@ -560,7 +562,7 @@ batchLLM_shiny <- function() {
               prompt = input$prompt,
               batch_delay = input$toggle_delay,
               batch_size = input$batch_size,
-              extract_XML = as.logical(input$extract_XML),
+              sanitize = as.logical(input$sanitize),
               model = config$model,
               temperature = config$temperature,
               max_tokens = config$max_tokens,
diff --git a/cran-comments.md b/cran-comments.md
index c31fff6..a5582b4 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,5 +1,19 @@
-## R CMD check results
+## Resubmission (10/09/2024)
 
-0 errors | 0 warnings | 0 notes
+This is a resubmission. In this version I have:
 
-* This is a new release.
+-   Written package names, software names, and API names in single quotes in the title and description fields as requested. For example: 'GPT', 'Claude', 'Gemini', 'shiny'. Also ensured that package names are case-sensitive where applicable.
+
+-   Provided links to the web services/APIs/LLMs used in the DESCRIPTION file. The links are now formatted correctly with angle brackets for auto-linking (e.g., `<https://chat.openai.com>`).
+
+-   Added `\value` tags to the `.Rd` files for exported methods, including documentation of the structure and meaning of the output. For functions that do not return a value, I have used the format `\value{No return value.}` as suggested for `man/batchLLM_shiny.Rd`.
+
+-   Removed `cat()` as a method to write to the console. Where necessary, I have replaced `cat()` calls with `message()`, `warning()`, or `stop()` as appropriate in `R/batchLLM.R`.
+
+All feedback has been addressed, and I have made the necessary adjustments for resubmission.
+
+## R CMD check results (10/05/2024)
+
+0 errors \| 0 warnings \| 0 notes
+
+-   This is a new release.
diff --git a/man/batchLLM.Rd b/man/batchLLM.Rd
index e2a48e7..c7760cd 100644
--- a/man/batchLLM.Rd
+++ b/man/batchLLM.Rd
@@ -15,11 +15,11 @@ batchLLM(
   max_tokens = 500,
   batch_delay = "random",
   batch_size = 10,
-  extract_XML = FALSE,
+  case_convert = NULL,
+  sanitize = FALSE,
   attempts = 1,
   log_name = "batchLLM-log",
   hash_algo = "crc32c",
-  case_convert = NULL,
   ...
 )
 }
@@ -44,7 +44,9 @@ batchLLM(
 
 \item{batch_size}{The number of rows to process in each batch. Default is 10.}
 
-\item{extract_XML}{Extract the LLM text completion from the model's response by returning only content in \code{<result>} XML tags. This helps prevent unwanted text (e.g., preamble) from being included in the model's output. Default is TRUE.}
+\item{case_convert}{A string for the case conversion of the output with the options: "upper", "lower", or NULL (no change). Default is NULL.}
+
+\item{sanitize}{Extract the LLM text completion from the model's response by returning only content in \code{<result>} XML tags. Additionally, remove all punctuation. This feature prevents unwanted text (e.g., preamble) or punctuation from being included in the model's output. Default is FALSE.}
 
 \item{attempts}{The maximum number of loop retry attempts. Default is 1.}
 
@@ -52,8 +54,6 @@ batchLLM(
 
 \item{hash_algo}{A string for a hashing algorithm from the 'digest' package. Default is \code{crc32c}.}
 
-\item{case_convert}{A string for the case conversion of the output with the options: "upper", "lower", or NULL (no change). Default is NULL.}
-
 \item{...}{Additional arguments to pass on to the LLM API function.}
 }
 \value{
diff --git a/man/batchLLM_shiny.Rd b/man/batchLLM_shiny.Rd
index c5094a0..d516b81 100644
--- a/man/batchLLM_shiny.Rd
+++ b/man/batchLLM_shiny.Rd
@@ -6,6 +6,9 @@
 \usage{
 batchLLM_shiny()
 }
+\value{
+No return value. Launches a Shiny Gadget that allows users to interact with the \code{batchLLM} package.
+}
 \description{
 This function provides a user interface using Shiny to interact with
 the \code{batchLLM} package. It allows users to configure and execute batch processing
diff --git a/readme.md b/readme.md
index 053ea8b..2ce668b 100644
--- a/readme.md
+++ b/readme.md
@@ -56,7 +56,8 @@ beliefs <- lapply(llm_configs, function(config) {
     max_tokens = 100,
     batch_size = 10,
     batch_delay = "1min",
-    case_convert = "lower"
+    case_convert = "lower",
+    sanitize = TRUE
   )
 })[[length(llm_configs)]]
 
@@ -66,7 +67,7 @@ print(beliefs)
 ## 🤖 Supported LLMs
 
 | LLM                                             | Models                                                                                                                        |
-|---------------|---------------------------------------------------------|
+|---------------------|---------------------------------------------------|
 | [OpenAI](https://github.com/irudnyts/openai)    | gpt-4, gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-3.5-turbo                                                                        |
 | [Anthropic](https://github.com/yrvelez/claudeR) | claude-3-5-sonnet-20240620, claude-3-opus-20240229, claude-3-sonnet-20240229, claude-3-haiku-20240307, claude-2.1, claude-2.0 |
 | [Google](https://github.com/jhk0530/gemini.R)   | 1.5-pro, 1.5-flash, 1.0-pro                                                                                                   |
@@ -82,6 +83,7 @@ print(beliefs)
 -   Sentiment analysis
 -   Thematic analysis
 -   Classification
+-   Re-factoring
 -   Labeling or tagging
 -   Language translation
 
@@ -102,7 +104,7 @@ This project is licensed under the [MIT License](LICENSE.md).
 
 ## 👨‍💻 Developer's Note
 
-My work on a complex classification problem inspired me to create this tool. I faced the challenge of categorizing thousands of unique offense descriptions in court data, and later, I tested the functionality to classify drug metabolites in toxicology data. The original function evolved significantly, and today, it powers this Shiny app designed to streamline and scale the use of LLMs across various datasets. I hope this tool proves as valuable to you as it has in my own projects.
+My work on a complex classification problem inspired me to create this tool. I was challenged with categorizing thousands of unique offense descriptions in court data, and later, I tested the functionality to classify drug metabolites to their drug categories in toxicology data. The original function evolved significantly, and today, it powers this Shiny app designed to streamline and scale the use of LLMs across various datasets. I hope this tool proves as valuable to you as it has in my own projects.
 
 ## 🔗 Links