Update fern/pages/v2/text-generation/retrieval-augmented-generation-r…

…ag.mdx

Co-authored-by: Meor Amer <[email protected]>
Signed-off-by: billytrend-cohere <[email protected]>

fern/pages/v2/text-generation/retrieval-augmented-generation-rag.mdx

@@ -206,7 +206,7 @@ instructions_verbose = "Write many search queries that will find helpful informa
 
 #### Step 2: Fetching relevant documents
 
-The next step is to [fetch documents](https://docs.cohere.com/docs/documents-and-citations) from the relevant data source using the generated search queries. For example, to answer the question about the two pop sensations _NSYNC_ and _Backstreet Boys_, one might want to use an API from a web search engine, and fetch the contents of the websites listed at the top of the search results.
+The next step is to fetch documents from the relevant data source using the generated search queries. For example, to answer the question about the two pop sensations _NSYNC_ and _Backstreet Boys_, one might want to use an API from a web search engine, and fetch the contents of the websites listed at the top of the search results.
 
 We won't go into details of fetching data in this guide, since it's very specific to the search API you're querying. However we should mention that breaking up long documents into smaller ones first (1-2 paragraphs) will help you not go over the context limit. When trying to stay within the context length limit, you might need to omit some of the documents from the request. To make sure that only the least relevant documents are omitted, we recommend using the [Rerank endpoint](https://docs.cohere.com/reference/rerank) endpoint which will sort the documents by relevancy to the query. The lowest ranked documents are the ones you should consider dropping first.