diff --git a/docs/docs/get-started/customization.md b/docs/docs/get-started/customization.md
index 24352431..90d40037 100644
--- a/docs/docs/get-started/customization.md
+++ b/docs/docs/get-started/customization.md
@@ -2,23 +2,24 @@
 
 Our Web Agents have the following modifiable elements:
 
-    - `llm`: The `LLM` used by the `Action Engine` to translate text instructions into automation code. You can set the `llm` to any `LlamaIndex LLM object`.
+- `llm`: The `LLM` used by the `Action Engine` to translate text instructions into automation code. You can set the `llm` to any `LlamaIndex LLM object`.
 
-    - `mm_llm`: The `multi-modal LLM` used by the `World Model` to generate the next instruction to be enacted by the Action Engine based on the current state of the web page. You can set the `mm_llm` argument to any `LlamaIndex LLM object`.
+- `mm_llm`: The `multi-modal LLM` used by the `World Model` to generate the next instruction to be enacted by the Action Engine based on the current state of the web page. You can set the `mm_llm` argument to any `LlamaIndex LLM object`.
 
-    - `embedding`: The `embedding model` is used by the `retriever` to convert segments of the HTML page of the target website into vectors, capturing semantic meaning. You can set this to any `LlamaIndex Embedding object`. 
+- `embedding`: The `embedding model` is used by the `retriever` to convert segments of the HTML page of the target website into vectors, capturing semantic meaning. You can set this to any `LlamaIndex Embedding object`. 
 
-    - `retriever`: The `retriever` is used within the `Action Model` to retrieve the most relevant HTML source code of the webpage to be able to generate the automation code targetting HTML elements. This can be any `LlamaIndex.retriever`.
+- `retriever`: The `retriever` is used within the `Action Model` to retrieve the most relevant HTML source code of the webpage to be able to generate the automation code targetting HTML elements. This can be any `LlamaIndex.retriever`.
 
-    - `prompt_template`: The prompt template used by the `Action Engine` to query the `LLM` in order to generate automation code for an instruction. You can replace this with your own prompt template as a string.
+- `prompt_template`: The prompt template used by the `Action Engine` to query the `LLM` in order to generate automation code for an instruction. You can replace this with your own prompt template as a string.
 
-    - `extractor`: The `cleaning function` applied to the automation code generated by the LLM in the `Action Engine`. You can replace this with your own custom method as a callable.
+- `extractor`: The `cleaning function` applied to the automation code generated by the LLM in the `Action Engine`. You can replace this with your own custom method as a callable.
 
 These elements are initialized in a `Context` object, which can optionally passed to both the `Action Engine` and `World Model` used by an Agent. If you don't pass them your own Context object, the default OpenaiContext will be used.
 
 ??? note "Default Configuration"
 
     The default configuration is as follows:
+    
     - `llm`: OpenAI's gpt-3.5-turbo,
     - `mm_llm`: OpenAi's gpt-4o,
     - `embedding`: text-embedding-3-large,
@@ -30,7 +31,7 @@ These elements are initialized in a `Context` object, which can optionally passe
 
 Let's take a look at how we can modify specific elements of an existing built-in Context.
 
-Example: Modifying an OpenaiContext
+#### Example: Modifying a built in context
 
 ```code
 python
@@ -66,7 +67,7 @@ Here, we modify the default `OpenaiContext` by replacing its LLM, multi-modal LL
 
 ## Creating a Context object from scratch
 
-Alternative, you can create a `Context` from scratch by initializing a `lavague.core.Context` and providing all the Context arguments: `llm`, `mm_llm`, `embedding`, `retriever`, `prompt_template` & `extractor`.
+Alternative, you can create a `Context` from scratch by initializing a `lavague.core.Context` object and providing all the Context arguments: `llm`, `mm_llm`, `embedding`, `retriever`, `prompt_template` & `extractor`.
 
 ## Summary