diff --git a/docs/pages/Guides/How-to-use-different-LLM.md b/docs/pages/Guides/How-to-use-different-LLM.md
index 8d7cccc..c300bef 100644
--- a/docs/pages/Guides/How-to-use-different-LLM.md
+++ b/docs/pages/Guides/How-to-use-different-LLM.md
@@ -1,36 +1,42 @@
-Fortunately, there are many providers for LLMs, and some of them can even be run locally.
+# Setting Up Local Language Models for Your App
 
-There are two models used in the app:
-1. Embeddings.
-2. Text generation.
+Your app relies on two essential models: Embeddings and Text Generation. While OpenAI's default models work seamlessly, you have the flexibility to switch providers or even run the models locally.
 
-By default, we use OpenAI's models, but if you want to change it or even run it locally, it's very simple!
+## Step 1: Configure Environment Variables
 
-### Go to .env file or set environment variables:
+Navigate to the `.env` file or set the following environment variables:
 
-`LLM_NAME=<your Text generation>`
+```env
+LLM_NAME=<your Text Generation model>
+API_KEY=<API key for Text Generation>
+EMBEDDINGS_NAME=<LLM for Embeddings>
+EMBEDDINGS_KEY=<API key for Embeddings>
+VITE_API_STREAMING=<true or false>
+```
 
-`API_KEY=<api_key for Text generation>`
+You can omit the keys if users provide their own. Ensure you set `LLM_NAME` and `EMBEDDINGS_NAME`.
 
-`EMBEDDINGS_NAME=<llm for embeddings>`
+## Step 2: Choose Your Models
 
-`EMBEDDINGS_KEY=<api_key for embeddings>`
+**Options for `LLM_NAME`:**
+- openai
+- manifest
+- cohere
+- Arc53/docsgpt-14b
+- Arc53/docsgpt-7b-falcon
+- llama.cpp
 
-`VITE_API_STREAMING=<true or false (true if using openai, false for all others)>`
+**Options for `EMBEDDINGS_NAME`:**
+- openai_text-embedding-ada-002
+- huggingface_sentence-transformers/all-mpnet-base-v2
+- huggingface_hkunlp/instructor-large
+- cohere_medium
 
-You don't need to provide keys if you are happy with users providing theirs, so make sure you set `LLM_NAME` and `EMBEDDINGS_NAME`.
+If using Llama, set `EMBEDDINGS_NAME` to `huggingface_sentence-transformers/all-mpnet-base-v2`. Download the required model and place it in the `models/` folder.
 
-Options:  
-LLM_NAME (openai, manifest, cohere, Arc53/docsgpt-14b, Arc53/docsgpt-7b-falcon, llama.cpp)  
-EMBEDDINGS_NAME (openai_text-embedding-ada-002, huggingface_sentence-transformers/all-mpnet-base-v2, huggingface_hkunlp/instructor-large, cohere_medium)
+Alternatively, for local Llama setup, run `setup.sh` and choose option 1. The script handles the DocsGPT model addition.
 
-If using Llama, set the `EMBEDDINGS_NAME` to `huggingface_sentence-transformers/all-mpnet-base-v2` and be sure to download [this model](https://d3dg1063dc54p9.cloudfront.net/models/docsgpt-7b-f16.gguf) into the `models/` folder: `https://d3dg1063dc54p9.cloudfront.net/models/docsgpt-7b-f16.gguf`. 
+## Step 3: Local Hosting for Privacy
 
-Alternatively, if you wish to run Llama locally, you can run `setup.sh` and choose option 1 when prompted. You do not need to manually add the DocsGPT model mentioned above to your `models/` folder if you use `setup.sh`, as the script will manage that step for you.
-
-That's it!
-
-### Hosting everything locally and privately (for using our optimised open-source models)
-If you are working with critical data and don't want anything to leave your premises.
-
-Make sure you set `SELF_HOSTED_MODEL` as true in your `.env` variable, and for your `LLM_NAME`, you can use anything that is on Hugging Face.
+If working with sensitive data, host everything locally by setting `SELF_HOSTED_MODEL` to true in your `.env`. For `LLM_NAME`, use any model available on Hugging Face.
+That's it! Your app is now configured for local and private hosting, ensuring optimal security for critical data.