diff --git a/examples/canopy-lib-quickstart.ipynb b/examples/canopy-lib-quickstart.ipynb
index d26cc376..b000fe16 100644
--- a/examples/canopy-lib-quickstart.ipynb
+++ b/examples/canopy-lib-quickstart.ipynb
@@ -589,9 +589,9 @@
    "source": [
     "## Query the Context Engine\n",
     "\n",
-    "`ContextEngine` is an object that responsible to retrieve the most relevant context for a given query and token budget.  \n",
+    "`ContextEngine` is an object responsible for retrieving the most relevant context for a given query and token budget.  \n",
     "\n",
-    "While `KnowledgeBase` retrieves the full `top-k` structured documents for each query including all the metadata related to them, the context engine in charge of transforming this information to a \"prompt ready\" context that can later feeded to an LLM. To achieve this the context engine holds a `ContextBuilder` object that takes query results from the knowledge base and returns a `Context` object. The context builder also considers the `max_context_tokens` budget given to it and builds the most relevant context that not exceeds the token budget."
+    "While `KnowledgeBase` retrieves the full `top-k` structured documents for each query including all the metadata related to them, the context engine in charge of transforming this information to a \"prompt ready\" context that can later feeded to an LLM. To achieve this the context engine holds a `ContextBuilder` object that takes query results from the knowledge base and returns a `Context` object. The `ContextEngine`'s default behavior is to use a `StuffingContextBuilder`, which simply stacks retrieved document chunks in a JSON-like manner, hard limiting by the number of chunks that fit the `max_context_tokens` budget. More complex behaviors can be achieved by providing a custom `ContextBuilder` class."
    ]
   },
   {