diff --git a/README.md b/README.md
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@@ -107,7 +107,3 @@ One terraform script to create a private network on openstack along with a route
 A Python script that when run, creates a filter word cloud from the summary of tickets over a time period.
 [More Here](word_cloud_generator/)
 
-## wex_llm_app
-
-Developed by Cloud WEX Students. A web application to host and interact with a large language model instance.
-[More Here](wex_llm_app)
diff --git a/wex_llm_app/README.md b/wex_llm_app/README.md
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-**Project Title:** End-end deployment of a Large Language Model web application
-
-**Project Overview:**
-
-Create a web application that allows users to send questions to a LLM backend and receive a response. The page will display the user's question, response and the response time.
-
-**Functional Requirements:**
-
-1. User Input:
-    * A text input field for users to enter questions.
-    * A submit button to send the question to the backend.
-2. LLM Backend:
-    * Intergrate with an Ollama LLM API instance using basic auth or tokens.
-    * Send streamed/completed responses to the users session.
-3. Response Display:
-    * Display the users question.
-    * Display the LLM response either streamed or complete.
-4. Response Time Measurment:
-    * Measure and display the round trip time (RTT) and generation time.
-5. Instructions for self-hosting:
-    * Provide simple setup instructions for a user to host their own LLM.
-
-**Non-Functional Requirements (Priority):**
-
-1. Performance (Medium):
-    * Ensure the webpage responds in a timely mannor. Think complete/streamed responses.
-2. Security (High):
-    * Implement basic auth or API tokens for authentication with the backend.
-    * Set up HTTPS using Let's Encrypt to encrypt traffic to the backend.
-3. Usability (Low):
-    * Design an intuitive interface for users to submit questions and view responses.
-
-**Technical Requirements:**
-
-1. Frontend:
-        * Build the webpage using HTML, CSS and JavaScript.
-        * Use native JavaScript APIs to make HTTP requests to the backend.
-2. Backend:
-        * Use the Ollama LLM API with authentication for receiving user questions and sending responses.
-        * (To be investigated) Use a database to store authentication information.
-3. Infrastructure:
-        * Use Terraform to provision multiple VM instances and networking.
-        * Configure machines/containers using Ansible playbooks to ensure consistency in deployment.
-        * Run the web server on multiple VM instances, each with its own Docker container.
-        * Use an Apache container to host the website within each Web VM.
-        * Set up a separate VM instance (with or without Docker) to run HAProxy and Let's Encrypt services.
-        * Configure HAProxy to load balance traffic across the multiple web server instances.
-        * Host the Ollama service directly on a separate GPU VM.
-      
-**Useful Resources:**
-
-1. [HTML, CSS, JS resources](https://developer.mozilla.org/en-US/)
-2. [HTML Tutorial](https://www.w3schools.com/html/)
-3. [CSS Tutorial](https://www.w3schools.com/css/)
-4. [JavaScript Tutorial](https://www.w3schools.com/js/)
-5. [Terraform Documentation](https://registry.terraform.io/providers/terraform-provider-openstack/openstack/latest/docs)
-6. [Ansible Documentation](https://docs.ansible.com/)
-7. [Docker Documnetation](https://docs.docker.com/)
-8. [Ollama Documentation](https://github.com/ollama/ollama)