diff --git a/R/bunch_request.R b/R/bunch_request.R
index 44fd78f..398a7aa 100644
--- a/R/bunch_request.R
+++ b/R/bunch_request.R
@@ -1,58 +1,131 @@
-gpt3.bunch_request = function(data
-                              , prompt_var
-                              , completion_var_name = 'gpt3_completion'
+#' Makes bunch completion requests to the GPT-3 API
+#'
+#' @description
+#' `gpt3_bunch_request()` is the package's main function for rquests and takes as input a vector of prompts and processes each prompt as per the defined parameters. It extends the `gpt3_simple_request()` function to allow for bunch processing of requests to the Open AI GPT-3 API.
+#' @details
+#' The easiest (and intended) use case for this function is to create a data.frame or data.table with variables that contain the prompts to be requested from GPT-3 and a prompt id (see examples below).
+#' For a general guide on the completion requests, see [https://beta.openai.com/docs/guides/completion](https://beta.openai.com/docs/guides/completion). This function provides you with an R wrapper to send requests with the full range of request parameters as detailed on [https://beta.openai.com/docs/api-reference/completions](https://beta.openai.com/docs/api-reference/completions) and reproduced below.
+#'
+#' For the `best_of` parameter: The `gpt3_simple_request()` (which is used here in a vectorised manner) handles the issue that best_of must be greater than n by setting if(best_of <= n){ best_of = n}.
+#'
+#' If `id_var` is not provided, the function will use `prompt_1` ... `prompt_n` as id variable.
+#'
+#' Parameters not included/supported:
+#'   - `logit_bias`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias](https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias)
+#'   - `echo`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-echo](https://beta.openai.com/docs/api-reference/completions/create#completions/create-echo)
+#'   - `stream`: [https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream](https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream)
+#'
+#' @param prompt_var character vector that contains the prompts to the GPT-3 request
+#' @param id_var (optional) character vector that contains the user-defined ids of the prompts. See details.
+#' @param param_model a character vector that indicates the [model](https://beta.openai.com/docs/models/gpt-3) to use; one of "text-davinci-002" (default), "text-curie-001", "text-babbage-001" or "text-ada-001"
+#' @param param_output_type character determining the output provided: "complete" (default), "text" or "meta"
+#' @param param_suffix character (default: NULL) (from the official API documentation: _The suffix that comes after a completion of inserted text_)
+#' @param param_max_tokens numeric (default: 100) indicating the maximum number of tokens that the completion request should return (from the official API documentation: _The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096)_)
+#' @param param_temperature numeric (default: 0.9) specifying the sampling strategy of the possible completions (from the official API documentation: _What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both._)
+#' @param param_top_p numeric (default: 1) specifying sampling strategy as an alternative to the temperature sampling (from the official API documentation: _An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both._)
+#' @param param_n numeric (default: 1) specifying the number of completions per request (from the official API documentation: _How many completions to generate for each prompt. **Note: Because this parameter generates many completions, it can quickly consume your token quota.** Use carefully and ensure that you have reasonable settings for max_tokens and stop._)
+#' @param param_logprobs numeric (default: NULL) (from the official API documentation: _Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response. The maximum value for logprobs is 5. If you need more than this, please contact support@openai.com and describe your use case._)
+#' @param param_stop character or character vector (default: NULL) that specifies after which character value when the completion should end (from the official API documentation: _Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence._)
+#' @param param_presence_penalty numeric (default: 0) between -2.00  and +2.00 to determine the penalisation of repetitiveness if a token already exists (from the official API documentation: _Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics._). See also: [https://beta.openai.com/docs/api-reference/parameter-details](https://beta.openai.com/docs/api-reference/parameter-details)
+#' @param param_frequency_penalty numeric (default: 0) between -2.00  and +2.00 to determine the penalisation of repetitiveness based on the frequency of a token in the text already (from the official API documentation: _Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim._). See also: [https://beta.openai.com/docs/api-reference/parameter-details](https://beta.openai.com/docs/api-reference/parameter-details)
+#' @param param_best_of numeric (default: 1) that determines the space of possibilities from which to select the completion with the highest probability (from the official API documentation: _Generates `best_of` completions server-side and returns the "best" (the one with the highest log probability per token)_). See details.
+#'
+#' @return A list with two data tables (if `param_output_type` is the default "complete"): [[1]] contains the data table with the columns `n` (= the mo. of `n` responses requested), `prompt` (= the prompt that was sent), `gpt3` (= the completion as returned from the GPT-3 model) and `id` (= the provided `id_var` or its default alternative). [[2]] contains the meta information of the request, including the request id, the parameters of the request and the token usage of the prompt (`tok_usage_prompt`), the completion (`tok_usage_completion`), the total usage (`tok_usage_total`), and the `id` (= the provided `id_var` or its default alternative).
+#'
+#' If `output_type` is "text", only the data table in slot [[1]] is returned.
+#'
+#' If `output_type` is "meta", only the data table in slot [[2]] is returned.
+#' @examples
+#' # First authenticate with your API key via `gpt3_authenticate('pathtokey')`
+#'
+#' # Once authenticated:
+#' # Assuming you have a data.table with 3 different prompts:
+#' dt_prompts = data.table::data.table('prompts' = c('What is the meaning if life?', 'Write a tweet about London:', 'Write a research proposal for using AI to fight fake news:'), 'prompt_id' = c(LETTERS[1:3]))
+#'gpt3_bunch_request(prompt_var = dt_prompts$prompts
+#'    , id_var = dt_prompts$prompt_id)
+#'
+#' ## With more controls
+#'gpt3_bunch_request(prompt_var = dt_prompts$prompts
+#'    , id_var = dt_prompts$prompt_id
+#'    , param_max_tokens = 50
+#'    , param_temperature = 0.5
+#'    , param_n = 5)
+#'
+#' ## Reproducible example (deterministic approach)
+#'gpt3_bunch_request(prompt_var = dt_prompts$prompts
+#'    , id_var = dt_prompts$prompt_id
+#'    , param_max_tokens = 50
+#'    , param_temperature = 0.0)
+#'
+#' ## Changing the GPT-3 model
+#'gpt3_bunch_request(prompt_var = dt_prompts$prompts
+#'    , id_var = dt_prompts$prompt_id
+#'    , param_model = 'text-babbage-001'
+#'    , param_max_tokens = 50
+#'    , param_temperature = 0.4)
+#' @export
+gpt3_bunch_request = function(prompt_var
+                              , id_var
+                              , param_output_type = 'complete'
                               , param_model = 'text-davinci-002'
                               , param_suffix = NULL
-                              , param_max_tokens = 256
+                              , param_max_tokens = 100
                               , param_temperature = 0.9
                               , param_top_p = 1
                               , param_n = 1
-                              , param_stream = F
                               , param_logprobs = NULL
-                              , param_echo = F
                               , param_stop = NULL
                               , param_presence_penalty = 0
                               , param_frequency_penalty = 0
-                              , param_best_of = 1
-                              , param_logit_bias = NULL){
+                              , param_best_of = 1){
 
+  data_length = length(prompt_var)
+  if(missing(id_var)){
+    data_id = paste0('prompt_', 1:data_length)
+  } else {
+    data_id = id_var
+  }
 
-  data_ = data
-
-  data_length = data_[, .N]
-
-  data_[, completion_name := '']
-
+  empty_list = list()
+  meta_list = list()
 
   for(i in 1:data_length){
 
     print(paste0('Request: ', i, '/', data_length))
 
-    row_outcome = gpt3.make_request(prompt = as.character(unname(data_[i, ..prompt_var]))
-                                    , model = param_model
-                                    , output_type = 'detail'
-                                    , suffix = param_suffix
-                                    , max_tokens = param_max_tokens
-                                    , temperature = param_temperature
-                                    , top_p = param_top_p
-                                    , n = param_n
-                                    , stream = param_stream
-                                    , logprobs = param_logprobs
-                                    , echo = param_echo
-                                    , stop = param_stop
-                                    , presence_penalty = param_presence_penalty
-                                    , frequency_penalty = param_frequency_penalty
-                                    , best_of = param_best_of
-                                    , logit_bias = param_logit_bias)
+    row_outcome = gpt3_simple_request(prompt_input = prompt_var[i]
+                                      , model = param_model
+                                      , output_type = 'complete'
+                                      , suffix = param_suffix
+                                      , max_tokens = param_max_tokens
+                                      , temperature = param_temperature
+                                      , top_p = param_top_p
+                                      , n = param_n
+                                      , logprobs = param_logprobs
+                                      , stop = param_stop
+                                      , presence_penalty = param_presence_penalty
+                                      , frequency_penalty = param_frequency_penalty
+                                      , best_of = param_best_of)
 
+    row_outcome[[1]]$id = data_id[i]
+    row_outcome[[2]]$id = data_id[i]
 
-    data_$completion_name[i] = row_outcome$choices[[1]]$text
-
+    empty_list[[i]] = row_outcome[[1]]
+    meta_list[[i]] = row_outcome[[2]]
 
   }
 
-  data_cols = ncol(data_)
-  names(data_)[data_cols] = completion_var_name
+  bunch_core_output = data.table::rbindlist(empty_list)
+  bunch_meta_output = data.table::rbindlist(meta_list)
 
-  return(data_)
+  if(param_output_type == 'complete'){
+    output = list(bunch_core_output
+                  , bunch_meta_output)
+  } else if(param_output_type == 'meta'){
+    output = bunch_meta_output
+  } else if(param_output_type == 'text'){
+    output = bunch_core_output
+  }
+
+  return(output)
 }