diff --git a/docs/src/embeddings/embedding_functions.md b/docs/src/embeddings/embedding_functions.md
index fd70cfc0..630c0e3f 100644
--- a/docs/src/embeddings/embedding_functions.md
+++ b/docs/src/embeddings/embedding_functions.md
@@ -1,7 +1,6 @@
-Representing multi-modal data as vector embeddings is becoming a standard practice. Embedding functions themselves be thought of as a part of the processing pipeline that each request(input) has to be passed through. After initial setup these components are not expected to change for a particular project. 
-
-This is main motivation behind our new embedding functions API, that allow you simply set it up once and the table remembers it, effectively making the **embedding functions disappear in the background** so you don't have to worry about modelling and simply focus on the DB aspects of VectorDB.
+Representing multi-modal data as vector embeddings is becoming a standard practice. Embedding functions themselves can be thought of as a part of the processing pipeline that each request(input) has to be passed through. After initial setup these components are not expected to change for a particular project. 
 
+Our new embedding functions API allow you simply set it up once and the table remembers it, effectively making the **embedding functions disappear in the background** so you don't have to worry about modelling and can simply focus on the DB aspects of VectorDB.
 
 You can simply follow these steps and forget about the details of your embedding functions as long as you don't intend to change it.
 
@@ -17,7 +16,7 @@ clip = registry.get("open-clip").create()
 You can also define your own embedding function by implementing the `EmbeddingFunction` abstract base interface. It subclasses PyDantic Model which can be utilized to write complex schemas simply as we'll see next!
 
 ### Step 2 - Define the Data Model or Schema
-Our embedding function from the previous section abstracts away all the details about the models and dimensions required to define the schema. You can simply set a feild as **source** or **vector** column. Here's how
+Our embedding function from the previous section abstracts away all the details about the models and dimensions required to define the schema. You can simply set a field as **source** or **vector** column. Here's how
 
 ```python
 from lancedb.pydantic import LanceModel, Vector