Fix typo (#194)

Signed-off-by: Gertjan "GJ" De Wilde <[email protected]>

fern/pages/cookbooks/agentic-multi-stage-rag.mdx

@@ -34,7 +34,7 @@ We ask three questions that require different depths of retrieval. We will see h
 
 ## Disclaimer
 
-One of the challenges in building a RAG system is that it has many moving pieces: vector database, type of embedding model, use of reranker, number of retrieved documents, chunking strategy, and more. These components can make debugging and evaluating RAG systems difficult. Since this notebook focuses on the concept of agentic RAG, it will simplify other parts of the RAG system. For example, we will only retrieve top 1 docuemnt to demonstrate what happens when retrieved document does not contain the answer needed.
+One of the challenges in building a RAG system is that it has many moving pieces: vector database, type of embedding model, use of reranker, number of retrieved documents, chunking strategy, and more. These components can make debugging and evaluating RAG systems difficult. Since this notebook focuses on the concept of agentic RAG, it will simplify other parts of the RAG system. For example, we will only retrieve top 1 document to demonstrate what happens when retrieved document does not contain the answer needed.
 
 ## Result
 
@@ -497,7 +497,7 @@ tools = [
 
 ```python PYTHON
 preamble2 = """# Instruction
-You are an expert assistant that helps users answers question about legal documents and policies.
+You are an expert assistant that helps users answer questions about legal documents and policies.
 
 Please follow these steps:
 1. Using user's query, use `retrieve_documents` tool to retrieve the most relevant document from the database.