diff --git a/R/gpt3_single_embedding.R b/R/gpt3_single_embedding.R
index dc6c2ea..ac0cee5 100644
--- a/R/gpt3_single_embedding.R
+++ b/R/gpt3_single_embedding.R
@@ -3,6 +3,7 @@
 #' @description
 #' `gpt3_single_embedding()` sends a single [embedding request](https://beta.openai.com/docs/guides/embeddings) to the Open AI GPT-3 API.
 #' @details The function supports the text similarity embeddings for the four GPT-3 models as specified in the parameter list. The main difference between the four models is the sophistication of the embedding representation as indicated by the vector embedding size.
+#'   - Second-generation embeddings model `text-embedding-ada-002` (1536 dimensions)
 #'   - Ada (1024 dimensions)
 #'   - Babbage (2048 dimensions)
 #'   - Curie (4096 dimensions)
@@ -12,7 +13,7 @@
 #'
 #' These vectors can be used for downstream tasks such as (vector) similarity calculations.
 #' @param input character that contains the text for which you want to obtain text embeddings from the GPT-3 model
-#' @param model a character vector that indicates the [similarity embedding model](https://beta.openai.com/docs/guides/embeddings/similarity-embeddings); one of "text-similarity-ada-001" (default), "text-similarity-curie-001", "text-similarity-babbage-001", "text-similarity-davinci-001"
+#' @param model a character vector that indicates the [similarity embedding model](https://beta.openai.com/docs/guides/embeddings/similarity-embeddings); one of "text-embedding-ada-002" (default), "text-similarity-ada-001", "text-similarity-curie-001", "text-similarity-babbage-001", "text-similarity-davinci-001". Note: it is strongly recommend to use the faster, cheaper and higher quality second generation embeddings model "text-embedding-ada-002".
 #' @return A numeric vector (= the embedding vector)
 #' @examples
 #' # First authenticate with your API key via `gpt3_authenticate('pathtokey')`
@@ -28,7 +29,7 @@
 #'     , model = 'text-similarity-curie-001')
 #' @export
 gpt3_single_embedding = function(input
-                               , model = 'text-similarity-ada-001'
+                               , model = 'text-embedding-ada-002'
                                ){
 
   parameter_list = list(model = model