diff --git a/R/make_request.R b/R/make_request.R
index f6c135c..a8fe10a 100644
--- a/R/make_request.R
+++ b/R/make_request.R
@@ -1,58 +1,140 @@
-#' Make a test request to the GPT-3 API
+#' Makes a single completion request to the GPT-3 API
 #'
 #' @description
-#' `gpt3.test_request()` sends a basic [completion request](https://beta.openai.com/docs/api-reference/completions) to the Open AI GPT-3 API.
-#' @param verbose (boolean) if TRUE prints the actual prompt and GPT-3 completion of the test request (default: FALSE).
-#' @return A message of success or failure of the connection.
+#' `gpt3_single_request()` sends a single [completion request](https://beta.openai.com/docs/api-reference/completions) to the Open AI GPT-3 API.
+#' @details 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: When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n. Note that this is handled by the wrapper automatically   if(best_of <= n){ best_of = n}.
+#'
+#' 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_input character that contains the prompt to the GPT-3 request
+#' @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 output_type character determining the output provided: "complete" (default), "text" or "meta"
+#' @param suffix character (default: NULL) (from the official API documentation: _The suffix that comes after a completion of inserted text_)
+#' @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 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 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 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 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 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 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 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 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 `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), and `gpt3` (= the completion as returned from the GPT-3 model). [[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`) and the total usage (`tok_usage_total`).
+#'
+#' 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
-#' gpt3.test_request()
+#' # First authenticate with your API key via `gpt3_authenticate('pathtokey')`
+#'
+#' # Once authenticated:
+#'
+#' ## Simple request with defaults:
+#' gpt3_simple_request(prompt_input = 'How old are you?')
+#'
+#' ## Instruct GPT-3 to write ten research ideas of max. 150 tokens with some controls:
+#'gpt3_simple_request(prompt_input = 'Write a research idea about using text data to understand human behaviour:', temperature = 0.8, n = 10, max_tokens = 150)
+#'
+#' ## For fully reproducible results, we need `temperature = 0`, e.g.:
+#' gpt3_simple_request(prompt_input = 'Finish this sentence:/n There is no easier way to learn R than', temperature = 0.0, max_tokens = 50)
+#'
+#' ## The same example with a different GPT-3 model:
+#' gpt3_simple_request(prompt_input = 'Finish this sentence:/n There is no easier way to learn R than', model = 'text-babbage-001', temperature = 0.0, max_tokens = 50)
 #' @export
-gpt3.make_request = function(prompt_
-                             , model_ = 'text-davinci-002'
-                             , output_type_ = 'string_only'
-                             , suffix_ = NULL
-                             , max_tokens_ = 256
-                             , temperature_ = 0.9
-                             , top_p_ = 1
-                             , n_ = 1
-                             , stream_ = F
-                             , logprobs_ = NULL
-                             , echo_ = F
-                             , stop_ = NULL
-                             , presence_penalty_ = 0
-                             , frequency_penalty_ = 0
-                             , best_of_ = 1
-                             , logit_bias_ = NULL
-)
-{
+gpt3_simple_request = function(prompt_input
+                              , model = 'text-davinci-002'
+                              , output_type = 'complete'
+                              , suffix = NULL
+                              , max_tokens = 100
+                              , temperature = 0.9
+                              , top_p = 1
+                              , n = 1
+                              , logprobs = NULL
+                              , stop = NULL
+                              , presence_penalty = 0
+                              , frequency_penalty = 0
+                              , best_of = 1){
 
-  parameter_list = list(prompt = prompt_
-                        , model = model_
-                        , suffix = suffix_
-                        , max_tokens = max_tokens_
-                        , temperature = temperature_
-                        , top_p = top_p_
-                        , n = n_
-                        , stream = stream_
-                        , logprobs = logprobs_
-                        , echo = echo_
-                        , stop = stop_
-                        , presence_penalty = presence_penalty_
-                        , frequency_penalty = frequency_penalty_
-                        , best_of = best_of_
-                        , logit_bias = logit_bias_
-  )
+  #check for request issues with `n` and `best_of`
+  if(best_of < n){
+    best_of = n
+    message('To avoid an `invalid_request_error`, `best_of` was set to equal `n`')
+  }
+
+  if(temperature == 0 & n > 1){
+    n = 1
+    message('You are running the deterministic model, so `n` was set to 1 to avoid unnecessary token quota usage.')
+  }
+
+  parameter_list = list(prompt = prompt_input
+                        , model = model
+                        , suffix = suffix
+                        , max_tokens = max_tokens
+                        , temperature = temperature
+                        , top_p = top_p
+                        , n = n
+                        , logprobs = logprobs
+                        , stop = stop
+                        , presence_penalty = presence_penalty
+                        , frequency_penalty = frequency_penalty
+                        , best_of = best_of)
 
   request_base = httr::POST(url = url.completions
-                      , body = parameter_list
-                      , httr::add_headers(Authorization = paste("Bearer", api_key))
-                      , encode = "json")
+                            , body = parameter_list
+                            , httr::add_headers(Authorization = paste("Bearer", api_key))
+                            , encode = "json")
+
+  request_content = httr::content(request_base)
+
+  if(n == 1){
+    core_output = data.table::data.table('n' = 1
+                                         , 'prompt' = prompt_input
+                                         , 'gpt3' = request_content$choices[[1]]$text)
+  } else if(n > 1){
+
+    core_output = data.table::data.table('n' = 1:n
+                                         , 'prompt' = rep(prompt_input, n)
+                                         , 'gpt3' = rep("", n))
+
+    for(i in 1:n){
+      core_output$gpt3[i] = request_content$choices[[i]]$text
+    }
+
+  }
 
 
-  if(output_type_ == 'string_only'){
-    output = httr::content(request_base)$choices[[1]]$text
-  } else {
-    output = httr::content(request_base)
+  meta_output = data.table::data.table('request_id' = request_content$id
+                           , 'object' = request_content$object
+                           , 'model' = request_content$model
+                           , 'param_prompt' = prompt_input
+                           , 'param_model' = model
+                           , 'param_suffix' = suffix
+                           , 'param_max_tokens' = max_tokens
+                           , 'param_temperature' = temperature
+                           , 'param_top_p' = top_p
+                           , 'param_n' = n
+                           , 'param_logprobs' = logprobs
+                           , 'param_stop' = stop
+                           , 'param_presence_penalty' = presence_penalty
+                           , 'param_frequency_penalty' = frequency_penalty
+                           , 'param_best_of' = best_of
+                           , 'tok_usage_prompt' = request_content$usage$prompt_tokens
+                           , 'tok_usage_completion' = request_content$usage$completion_tokens
+                           , 'tok_usage_total' = request_content$usage$total_tokens)
+
+  if(output_type == 'complete'){
+    output = list(core_output
+                  , meta_output)
+  } else if(output_type == 'meta'){
+    output = meta_output
+  } else if(output_type == 'text'){
+    output = core_output
   }
 
   return(output)