diff --git a/docs/getting_started/concepts.md b/docs/getting_started/concepts.md
index e71686753d3e290e079559b15009e91769046f00..3dc1097a541568e6b36475e3dea7010804273cb8 100644
--- a/docs/getting_started/concepts.md
+++ b/docs/getting_started/concepts.md
@@ -10,7 +10,7 @@ If you haven't, [install LlamaIndex](/getting_started/installation.md) and compl
 
 LLMs are trained on enormous bodies of data but they aren't trained on **your** data. Retrieval-Augmented Generation (RAG) solves this problem by adding your data to the data LLMs already have access to. You will see references to RAG frequently in this documentation.
 
-In RAG, your data is loaded and and prepared for queries or "indexed". User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response.
+In RAG, your data is loaded and prepared for queries or "indexed". User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response.
 
 Even if what you're building is a chatbot or an agent, you'll want to know RAG techniques for getting data into your application.
 
@@ -55,6 +55,9 @@ Once you've ingested your data, LlamaIndex will help you index the data into a s
 [**Retrievers**](/module_guides/querying/retriever/root.md):
 A retriever defines how to efficiently retrieve relevant context from an index when given a query. Your retrieval strategy is key to the relevancy of the data retrieved and the efficiency with which it's done.
 
+[**Routers**](/module_guides/querying/router/root.md):
+A router determines which retriever will be used to retrieve relevant context from the knowledge base. More specifically, the `RouterRetriever` class, are responsible for selecting one or multiple candidate retrievers to execute a query. They use a selector to choose the best option based on each candidate's metadata and the query.
+
 [**Node Postprocessors**](/module_guides/querying/node_postprocessors/root.md):
 A node postprocessor takes in a set of retrieved nodes and applies transformations, filtering, or re-ranking logic to them.