diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..4dc8d42
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,147 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# VSCode configuration
+.vscode
+
+# Evaluation function config
+config.json
+
+# README
+README.md
+
+# GitHub
+.github
+
+# Data folder
+data/
+
+# Test reports
+reports/
\ No newline at end of file
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
new file mode 100644
index 0000000..bc1e9f5
--- /dev/null
+++ b/.github/workflows/build.yml
@@ -0,0 +1,12 @@
+name: Build Evaluation Function Image
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+  workflow_dispatch:
+
+jobs:
+  build:
+    uses: lambda-feedback/evaluation-function-workflows/.github/workflows/build.yml@main
diff --git a/.github/workflows/production-deploy.yml b/.github/workflows/production-deploy.yml
new file mode 100644
index 0000000..ac5b7b9
--- /dev/null
+++ b/.github/workflows/production-deploy.yml
@@ -0,0 +1,33 @@
+name: Deploy Production Version to Lambda Feedback
+
+on:
+  workflow_dispatch:
+    inputs:
+      version-bump:
+        description: 'Version bump type'
+        required: true
+        type: choice
+        options:
+          - patch
+          - minor
+          - major
+        default: patch
+      branch:
+        description: 'Branch to release from'
+        required: true
+        type: string
+        default: 'main'
+jobs:
+  deploy:
+    uses: lambda-feedback/evaluation-function-workflows/.github/workflows/deploy.yml@deploy-request
+    with:
+      template-repository-name: 'lambda-feedback/evaluation-function-boilerplate-wolfram'
+      build-platforms: 'gcp'
+      environment: "production"
+      version-bump: ${{ inputs.version-bump }}
+      branch: ${{ inputs.branch }}
+    secrets:
+      aws-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
+      aws-secret-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET}}
+      function-admin-api-key: ${{ secrets.FUNCTION_ADMIN_API_KEY}}
+      gcp_credentials: ${{ secrets.GCP_DEPLOY }}
diff --git a/.github/workflows/staging-deploy.yml b/.github/workflows/staging-deploy.yml
new file mode 100644
index 0000000..bd00111
--- /dev/null
+++ b/.github/workflows/staging-deploy.yml
@@ -0,0 +1,21 @@
+name: Deploy to Lambda Feedback
+
+on:
+  push:
+    branches:
+      - main
+      - master
+  workflow_dispatch:
+
+jobs:
+  deploy:
+    uses: lambda-feedback/evaluation-function-workflows/.github/workflows/deploy.yml@deploy-request
+    with:
+      template-repository-name: 'lambda-feedback/evaluation-function-boilerplate-wolfram'
+      build-platforms: 'gcp'
+      environment: "staging"
+    secrets:
+      aws-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
+      aws-secret-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET}}
+      function-admin-api-key: ${{ secrets.FUNCTION_ADMIN_API_KEY}}
+      gcp_credentials: ${{ secrets.GCP_DEPLOY }}
diff --git a/.github/workflows/test-and-deploy.yml b/.github/workflows/test-and-deploy.yml
deleted file mode 100755
index 362c8f8..0000000
--- a/.github/workflows/test-and-deploy.yml
+++ /dev/null
@@ -1,192 +0,0 @@
-name: Test & Deploy Evaluation Function to AWS Lambda
-
-on:
-  push:
-    branches:
-      - master
-      - main
-  workflow_dispatch:
-
-jobs:
-  test:
-    name: Test
-    runs-on: ubuntu-latest
-
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: [3.8]
-
-    defaults:
-      run:
-        working-directory: app/
-
-    env:
-      REQUEST_SCHEMA_URL: https://raw.githubusercontent.com/lambda-feedback/request-response-schemas/master/request.json
-      RESPONSE_SCHEMA_URL: https://raw.githubusercontent.com/lambda-feedback/request-response-schemas/master/responsev2.json
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      TEST_KEY: ${{ secrets.TEST_KEY }}
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v2
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install flake8 pytest
-          python -m pip install -r requirements.txt
-
-      - name: Lint with flake8
-        run: |
-          # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-
-      - name: Test Evaluation Function
-        run: |
-          pytest -v evaluation_tests.py::TestEvaluationFunction
-
-  deploy-staging:
-    name: Deploy Staging
-    needs: test
-    runs-on: ubuntu-latest
-    environment: production
-    env:
-      ECR_REPOSITORY: lambda-feedback-staging-functions-repository
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v2
-
-      - name: Set config.json output
-        id: set_config_var
-        run: |
-          content=`cat ./config.json`
-          # the following lines are only required for multi line json
-          content="${content//'%'/'%25'}"
-          content="${content//$'\n'/'%0A'}"
-          content="${content//$'\r'/'%0D'}"
-          # end of optional handling for multi line json
-          echo "::set-output name=configJson::$content"
-
-      - name: set Evaluation Function Name
-        id: set_function_name
-        run: |
-          functionName="${{fromJson(steps.set_config_var.outputs.configJson).EvaluationFunctionName}}"
-          [[ -z "$functionName" ]] && { echo "Add EvaluationFunctionName to config.json" ; exit 1; }
-          echo "::set-output name=function_name::$functionName"
-
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v1
-        with:
-          aws-access-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
-          aws-secret-access-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET }}
-          aws-region: eu-west-2
-
-      - name: Login to Amazon ECR
-        id: login-ecr
-        uses: aws-actions/amazon-ecr-login@v1
-
-      - name: Build, tag, and push image to Amazon ECR
-        id: build-image
-        env:
-          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
-          IMAGE_TAG: ${{ steps.set_function_name.outputs.function_name }}
-        run: |
-          # Build docker image from algorithm, schema and requirements
-          docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG app/
-          # Push image to ECR
-          docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
-          echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
-
-      - name: deploy evaluation function
-        id: deploy-evaluation-function
-        env:
-          BACKEND_API_URL: https://staging-api.lambdafeedback.com
-          API_KEY: ${{ secrets.FUNCTION_ADMIN_API_KEY }}
-          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
-          IMAGE_TAG: ${{ steps.set_function_name.outputs.function_name }}
-        run: |
-          curl --location --request POST "$BACKEND_API_URL/grading-function/ensure" \
-          --header 'content-type: application/json' \
-          --data-raw "{
-              \"apiKey\": \"$API_KEY\",
-              \"dockerImageUri\": \"$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG\",
-              \"functionName\": \"$IMAGE_TAG\"
-          }"
-
-  deploy-production:
-    name: Deploy Production
-    needs: deploy-staging
-    runs-on: ubuntu-latest
-    environment: production
-    env:
-      ECR_REPOSITORY: lambda-feedback-production-functions-repository
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v2
-
-      - name: Set config.json output
-        id: set_config_var
-        run: |
-          content=`cat ./config.json`
-          # the following lines are only required for multi line json
-          content="${content//'%'/'%25'}"
-          content="${content//$'\n'/'%0A'}"
-          content="${content//$'\r'/'%0D'}"
-          # end of optional handling for multi line json
-          echo "::set-output name=configJson::$content"
-
-      - name: set Evaluation Function Name
-        id: set_function_name
-        run: |
-          functionName="${{fromJson(steps.set_config_var.outputs.configJson).EvaluationFunctionName}}"
-          [[ -z "$functionName" ]] && { echo "Add EvaluationFunctionName to config.json" ; exit 1; }
-          echo "::set-output name=function_name::$functionName"
-
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v1
-        with:
-          aws-access-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
-          aws-secret-access-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET }}
-          aws-region: eu-west-2
-
-      - name: Login to Amazon ECR
-        id: login-ecr
-        uses: aws-actions/amazon-ecr-login@v1
-
-      - name: Build, tag, and push image to Amazon ECR
-        id: build-image
-        env:
-          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
-          IMAGE_TAG: ${{ steps.set_function_name.outputs.function_name }}
-        run: |
-          # Build docker image from algorithm, schema and requirements
-          docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG app/
-          # Push image to ECR
-          docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
-          echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
-
-      - name: deploy evaluation function
-        id: deploy-evaluation-function
-        env:
-          BACKEND_API_URL: https://prod-api.lambdafeedback.com
-          API_KEY: ${{ secrets.FUNCTION_ADMIN_API_KEY }}
-          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
-          IMAGE_TAG: ${{ steps.set_function_name.outputs.function_name }}
-        run: |
-          curl --location --request POST "$BACKEND_API_URL/grading-function/ensure" \
-          --header 'content-type: application/json' \
-          --data-raw "{
-              \"apiKey\": \"$API_KEY\",
-              \"dockerImageUri\": \"$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG\",
-              \"functionName\": \"$IMAGE_TAG\"
-          }"
diff --git a/.gitignore b/.gitignore
index b6e4761..9a1a0ae 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,3 +127,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+.env
+.DS_Store
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100755
index 0000000..bae15a9
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,37 @@
+FROM ghcr.io/lambda-feedback/evaluation-function-base/python:3.12 AS builder
+
+RUN pip install poetry==1.8.3
+
+ENV POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_IN_PROJECT=1 \
+    POETRY_VIRTUALENVS_CREATE=1 \
+    POETRY_CACHE_DIR=/tmp/poetry_cache
+
+COPY pyproject.toml poetry.lock ./
+
+RUN --mount=type=cache,target=$POETRY_CACHE_DIR \
+    poetry install --without dev --no-root
+
+FROM ghcr.io/lambda-feedback/evaluation-function-base/python:3.12
+
+ENV VIRTUAL_ENV=/app/.venv \
+    PATH="/app/.venv/bin:$PATH"
+
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+
+# Precompile python files for faster startup
+RUN python -m compileall -q .
+
+# Copy the evaluation function to the app directory
+COPY evaluation_function ./evaluation_function
+
+# Command to start the evaluation function with
+ENV FUNCTION_COMMAND="python"
+
+# Args to start the evaluation function with
+ENV FUNCTION_ARGS="-m,evaluation_function.main"
+
+# The transport to use for the RPC server
+#ENV FUNCTION_RPC_TRANSPORT="ipc"
+ENV FUNCTION_INTERFACE="file"
+ENV LOG_LEVEL="debug"
\ No newline at end of file
diff --git a/README.md b/README.md
index c42f2cc..c1a47bc 100755
--- a/README.md
+++ b/README.md
@@ -2,90 +2,196 @@
 
 This function can be used to prompt GPT models by providing prompts in the request parameters. For more information, look at `app/docs`.
 
-## Table of Contents
-- [Evaluation Function Template Repository](#evaluation-function-template-repository)
-  - [Table of Contents](#table-of-contents)
-  - [Repository Structure](#repository-structure)
-  - [Usage](#usage)
-    - [Getting Started](#getting-started)
-  - [How it works](#how-it-works)
-    - [Docker & Amazon Web Services (AWS)](#docker--amazon-web-services-aws)
-    - [Middleware Functions](#middleware-functions)
-    - [GitHub Actions](#github-actions)
-  - [Pre-requisites](#pre-requisites)
-
-## Repository Structure
+## Development
+[![Create Release Request](https://img.shields.io/badge/Create%20Release%20Request-blue?style=for-the-badge)](https://github.com/lambda-feedback/langModels/issues/new?template=release-request.yml)
+
+
+## Usage
+
+You can run the evaluation function either using [the pre-built Docker image](#run-the-docker-image) or build and run [the binary executable](#build-and-run-the-binary).
+
+### Run the Docker Image
+
+The pre-built Docker image comes with [Shimmy](https://github.com/lambda-feedback/shimmy) installed.
+
+> [!TIP]
+> Shimmy is a small application that listens for incoming HTTP requests, validates the incoming data and forwards it to the underlying evaluation function. Learn more about Shimmy in the [Documentation](https://github.com/lambda-feedback/shimmy).
+
+The pre-built Docker image is available on the GitHub Container Registry. You can run the image using the following command:
 
 ```bash
-app/
-    __init__.py
-    evaluation.py # Script containing the main evaluation_function
-    docs.md # Documentation page for this function (required)
-    evaluation_tests.py # Unittests for the main evaluation_function
-    requirements.txt # list of packages needed for algorithm.py
-    Dockerfile # for building whole image to deploy to AWS
-
-.github/
-    workflows/
-        test-and-deploy.yml # Testing and deployment pipeline
-
-config.json # Specify the name of the evaluation function in this file
-.gitignore
+docker run -p 8080:8080 ghcr.io/lambda-feedback/evaluation-function-boilerplate-python:latest
 ```
 
-## Usage
+### Run the Script
+
+You can choose between running the Python evaluation function itself, ore using Shimmy to run the function.
 
-### Getting Started
+**Raw Mode**
 
-1. Clone this repository
-2. Change the name of the evaluation function in `config.json`
-3. The name must be unique. To view existing grading functions, go to:
+Use the following command to run the evaluation function directly:
 
-   - [Staging API Gateway Integrations](https://eu-west-2.console.aws.amazon.com/apigateway/main/develop/integrations/attach?api=c1o0u8se7b&region=eu-west-2&routes=0xsoy4q)
-   - [Production API Gateway Integrations](https://eu-west-2.console.aws.amazon.com/apigateway/main/develop/integrations/attach?api=cttolq2oph&integration=qpbgva8&region=eu-west-2&routes=0xsoy4q)
+```bash
+python -m evaluation_function.main
+```
+
+This will run the evaluation function using the input data from `request.json` and write the output to `response.json`.
 
-4. Merge commits into the default branch
-   - This will trigger the `test-and-deploy.yml` workflow, which will build the docker image, push it to a shared ECR repository, then call the backend `grading-function/ensure` route to build the necessary infrastructure to make the function available from the client app.
+**Shimmy**
 
-5. You are now ready to start developing your function:
-   
-   - Edit the `app/evaluation.py` file, which ultimately gets called when the function is given the `eval` command
-   - Edit the `app/evaluation_tests.py` file to add tests which get run:
-       - Every time you commit to this repo, before the image is built and deployed 
-       - Whenever the `healthcheck` command is supplied to the deployed function
-   - Edit the `app/docs.md` file to reflect your changes. This file is baked into the function's image, and is made available using the `docs` command. This feature is used to display this function's documentation on our [Documentation](https://lambda-feedback.github.io/Documentation/) website once it's been hooked up!
+To have a more user-friendly experience, you can use [Shimmy](https://github.com/lambda-feedback/shimmy) to run the evaluation function.
 
----
+To run the evaluation function using Shimmy, use the following command:
+
+```bash
+shimmy -c "python" -a "-m" -a "evaluation_function.main" -i ipc
+```
 
-## How it works
+## Development
 
-The function is built on top of a custom base layer, [BaseEvaluationFunctionLayer](https://github.com/lambda-feedback/BaseEvalutionFunctionLayer), which tools, tests and schema checking relevant to all evaluation functions.
+### Prerequisites
 
-### Docker & Amazon Web Services (AWS)
+- [Docker](https://docs.docker.com/get-docker/)
+- [Python](https://www.python.org)
 
-The grading scripts are hosted AWS Lambda, using containers to run a docker image of the app. Docker is a popular tool in software development that allows programs to be hosted on any machine by bundling all its requirements and dependencies into a single file called an **image**.
+### Repository Structure
 
-Images are run within **containers** on AWS, which give us a lot of flexibility over what programming language and packages/libraries can be used. For more information on Docker, read this [introduction to containerisation](https://www.freecodecamp.org/news/a-beginner-friendly-introduction-to-containers-vms-and-docker-79a9e3e119b/). To learn more about AWS Lambda, click [here](https://geekflare.com/aws-lambda-for-beginners/).
+```bash
+.github/workflows/
+    build.yml                           # builds the public evaluation function image
+    deploy.yml                          # deploys the evaluation function to Lambda Feedback
 
-### Middleware Functions
-In order to run the algorithm and schema on AWS Lambda, some middleware functions have been provided to handle, validate and return the data so all you need to worry about is the evaluation script and testing.
+evaluation_function/main.py             # evaluation function entrypoint
+evaluation_function/evaluation.py       # evaluation function implementation
+evaluation_function/evaluation_test.py  # evaluation function tests
+evaluation_function/preview.py          # evaluation function preview
+evaluation_function/preview_test.py     # evaluation function preview tests
 
-The code needed to build the image using all the middleware functions are available in the [BaseEvaluationFunctionLayer](https://github.com/lambda-feedback/BaseEvalutionFunctionLayer) repository.
+config.json                             # evaluation function deployment configuration file
+```
 
-### GitHub Actions
-Whenever a commit is made to the GitHub repository, the new code will go through a pipeline, where it will be tested for syntax errors and code coverage. The pipeline used is called **GitHub Actions** and the scripts for these can be found in `.github/workflows/`.
+### Development Workflow
 
-On top of that, when starting a new evaluation function, you will have to complete a set of unit test scripts, which not only make sure your code is reliable, but also helps you to build a _specification_ for how the code should function before you start programming.
+In its most basic form, the development workflow consists of writing the evaluation function in the `evaluation_function.wl` file and testing it locally. As long as the evaluation function adheres to the Evaluation Function API, a development workflow which incorporates using Shimmy is not necessary.
 
-Once the code passes all these tests, it will then be uploaded to AWS and will be deployed and ready to go in only a few minutes.
+Testing the evaluation function can be done by running the `dev.py` script using the Python interpreter like so:
 
-## Pre-requisites
-Although all programming can be done through the GitHub interface, it is recommended you do this locally on your machine. To do this, you must have installed:
+```bash
+python -m evaluation_function.dev <response> <answer>
+```
 
-- Python 3.8 or higher.
+> [!NOTE]
+> Specify the `response` and `answer` as command-line arguments.
 
-- GitHub Desktop or the `git` CLI.
+### Building the Docker Image
 
-- A code editor such as Atom, VS Code, or Sublime.
+To build the Docker image, run the following command:
+
+```bash
+docker build -t my-python-evaluation-function .
+```
+
+### Running the Docker Image
+
+To run the Docker image, use the following command:
+
+```bash
+docker run -it --rm -p 8080:8080 my-python-evaluation-function
+```
+
+This will start the evaluation function and expose it on port `8080`.
+
+## Deployment
+
+This section guides you through the deployment process of the evaluation function. If you want to deploy the evaluation function to Lambda Feedback, follow the steps in the [Lambda Feedback](#deploy-to-lambda-feedback) section. Otherwise, you can deploy the evaluation function to other platforms using the [Other Platforms](#deploy-to-other-platforms) section.
+
+### Deploy to Lambda Feedback
+
+Deploying the evaluation function to Lambda Feedback is simple and straightforward, as long as the repository is within the [Lambda Feedback organization](https://github.com/lambda-feedback).
+
+After configuring the repository, a [GitHub Actions workflow](.github/workflows/deploy.yml) will automatically build and deploy the evaluation function to Lambda Feedback as soon as changes are pushed to the main branch of the repository.
+
+**Configuration**
+
+The deployment configuration is stored in the `config.json` file. Choose a unique name for the evaluation function and set the `EvaluationFunctionName` field in [`config.json`](config.json).
+
+> [!IMPORTANT]
+> The evaluation function name must be unique within the Lambda Feedback organization, and must be in `lowerCamelCase`. You can find a example configuration below:
+
+```json
+{
+  "EvaluationFunctionName": "compareStringsWithPython"
+}
+```
+
+### Deploy to other Platforms
+
+If you want to deploy the evaluation function to other platforms, you can use the Docker image to deploy the evaluation function.
+
+Please refer to the deployment documentation of the platform you want to deploy the evaluation function to.
+
+If you need help with the deployment, feel free to reach out to the Lambda Feedback team by creating an issue in the template repository.
+
+## FAQ
+
+### Pull Changes from the Template Repository
+
+If you want to pull changes from the template repository to your repository, follow these steps:
+
+1. Add the template repository as a remote:
+
+```bash
+git remote add template https://github.com/lambda-feedback/evaluation-function-boilerplate-python.git
+```
+
+2. Fetch changes from all remotes:
+
+```bash
+git fetch --all
+```
+
+3. Merge changes from the template repository:
+
+```bash
+git merge template/main --allow-unrelated-histories
+```
+
+> [!WARNING]
+> Make sure to resolve any conflicts and keep the changes you want to keep.
+
+## Troubleshooting
+
+### Containerized Evaluation Function Fails to Start
+
+If your evaluation function is working fine when run locally, but not when containerized, there is much more to consider. Here are some common issues and solution approaches:
+
+**Run-time dependencies**
+
+Make sure that all run-time dependencies are installed in the Docker image.
+
+- Python packages: Make sure to add the dependency to the `pyproject.toml` file, and run `poetry install` in the Dockerfile.
+- System packages: If you need to install system packages, add the installation command to the Dockerfile.
+- ML models: If your evaluation function depends on ML models, make sure to include them in the Docker image.
+- Data files: If your evaluation function depends on data files, make sure to include them in the Docker image.
+
+**Architecture**
+
+Some package may not be compatible with the architecture of the Docker image. Make sure to use the correct platform when building and running the Docker image.
+
+E.g. to build a Docker image for the `linux/x86_64` platform, use the following command:
+
+```bash
+docker build --platform=linux/x86_64 .
+```
+
+**Verify Standalone Execution**
+
+If requests are timing out, it might be due to the evaluation function not being able to run. Make sure that the evaluation function can be run as a standalone script. This will help you to identify issues that are specific to the containerized environment.
+
+To run just the evaluation function as a standalone script, without using Shimmy, use the following command:
+
+```bash
+docker run -it --rm my-python-evaluation-function python -m evaluation_function.main
+```
 
-Copy this template over by clicking **Use this template** button found in the repository on GitHub. Save it to the `lambda-feedback` Organisation.
+If the command starts without any errors, the evaluation function is working correctly. If not, you will see the error message in the console.
diff --git a/app/.gitignore b/app/.gitignore
deleted file mode 100644
index 4c842e4..0000000
--- a/app/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-.env
-.DS_Store
\ No newline at end of file
diff --git a/app/Dockerfile b/app/Dockerfile
deleted file mode 100755
index b1d9165..0000000
--- a/app/Dockerfile
+++ /dev/null
@@ -1,24 +0,0 @@
-# Base image that bundles AWS Lambda Python 3.8 image with some middleware functions
-# FROM base-eval-tmp
-FROM ghcr.io/lambda-feedback/baseevalutionfunctionlayer:main-3.8
-
-WORKDIR /app
-
-# Copy and install any packages/modules needed for your evaluation script.
-COPY requirements.txt .
-RUN pip3 install -r requirements.txt
-
-# Copy the evaluation and testing scripts
-COPY evaluation.py ./app/
-COPY evaluation_tests.py ./app/
-
-# Copy Documentation
-COPY docs/dev.md ./app/docs/dev.md
-COPY docs/user.md ./app/docs/user.md
-
-# Set permissions so files and directories can be accessed on AWS
-RUN chmod 644 $(find . -type f)
-RUN chmod 755 $(find . -type d)
-
-# The entrypoint for AWS is to invoke the handler function within the app package
-CMD [ "/app/app.handler" ]
diff --git a/app/requirements.txt b/app/requirements.txt
deleted file mode 100644
index 29e558b..0000000
--- a/app/requirements.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-aiohttp==3.9.5
-aiosignal==1.3.1
-attrs==23.2.0
-certifi==2024.6.2
-charset-normalizer==3.3.2
-frozenlist==1.4.1
-idna==3.7
-iniconfig==2.0.0
-multidict==6.0.5
-openai==0.28.0
-packaging==24.1
-pluggy==1.5.0
-pytest==8.2.2
-python-dotenv==0.21.1
-requests==2.32.3
-tqdm==4.66.4
-urllib3==2.2.1
-yarl==1.9.4
diff --git a/app/docs/dev.md b/docs/dev.md
similarity index 100%
rename from app/docs/dev.md
rename to docs/dev.md
diff --git a/app/docs/user.md b/docs/user.md
similarity index 100%
rename from app/docs/user.md
rename to docs/user.md
diff --git a/app/__init__.py b/evaluation_function/__init__.py
similarity index 100%
rename from app/__init__.py
rename to evaluation_function/__init__.py
diff --git a/evaluation_function/dev.py b/evaluation_function/dev.py
new file mode 100644
index 0000000..dd70ad8
--- /dev/null
+++ b/evaluation_function/dev.py
@@ -0,0 +1,26 @@
+import sys
+
+from lf_toolkit.shared.params import Params
+
+from .evaluation import evaluation_function
+
+
+def dev():
+    """Run the evaluation function from the command line for development purposes.
+
+    Usage: python -m evaluation_function.dev <answer> <response>
+    """
+    if len(sys.argv) < 3:
+        print("Usage: python -m evaluation_function.dev <answer> <response>")
+        return
+
+    answer = sys.argv[1]
+    response = sys.argv[2]
+
+    result = evaluation_function(answer, response, Params())
+
+    print(result.to_dict())
+
+
+if __name__ == "__main__":
+    dev()
\ No newline at end of file
diff --git a/app/evaluation.py b/evaluation_function/evaluation.py
similarity index 80%
rename from app/evaluation.py
rename to evaluation_function/evaluation.py
index ea8ecbe..b8585e8 100755
--- a/app/evaluation.py
+++ b/evaluation_function/evaluation.py
@@ -1,9 +1,9 @@
 import os
-import openai
-import json
 from dotenv import load_dotenv
-
-load_dotenv()
+from typing import Any
+from lf_toolkit.evaluation import Result, Params
+from openai import OpenAI
+import openai
 
 # A basic way to call ChatGPT from the Lambda Feedback platform
 
@@ -14,7 +14,11 @@ def enforce_full_stop(s):
     return s
 
 
-def evaluation_function(response, answer, parameters):
+def evaluation_function(
+    response: Any,
+    answer: Any,
+    parameters: Params,
+) -> Result:
     """
     Function used to evaluate a student response.
     ---
@@ -40,8 +44,13 @@ def evaluation_function(response, answer, parameters):
     to output the evaluation response.
     """
 
+    print("Connecting to OpenAI")
     openai.api_key = os.environ.get("OPENAI_API_KEY")
 
+    client = OpenAI()
+
+    print("Connected to OpenAI")
+
     # Making sure that each prompt ends with a full stop (prevents gpt getting confused when concatenated)
     main_prompt = enforce_full_stop(parameters['main_prompt'])
     default_prompt = enforce_full_stop(parameters['default_prompt'])
@@ -50,25 +59,22 @@ def evaluation_function(response, answer, parameters):
     print(feedback_prompt)
 
     # Call openAI API for boolean
-    completion_boolean = openai.ChatCompletion.create(
+    completion_boolean = client.chat.completions.create(
         model=parameters['model'],
         messages=[{"role": "system", "content": main_prompt + " " + default_prompt},
                   {"role": "user", "content": response}])
 
-    is_correct = completion_boolean.choices[0].message.content.strip(
-    ) == "True"
-    is_correct_str = str(is_correct)
-
-    output = {"is_correct": is_correct}
+    is_correct = completion_boolean.choices[0].message.content.strip() == "True"
+    result = Result(is_correct=is_correct)
 
     # Check if feedback prompt is empty or not. Only populates feedback in 'output' if there is a 'feedback_prompt'.
     if parameters['feedback_prompt'].strip():
-        completion_feedback = openai.ChatCompletion.create(
+        completion_feedback = client.chat.completions.create(
             model=parameters['model'],
-            messages=[{"role": "system", "content": main_prompt + " " + feedback_prompt + " You must take the student's answer to be: " + is_correct_str},
+            messages=[{"role": "system", "content": main_prompt + " " + feedback_prompt + " You must take the student's answer to be: " + str(is_correct)},
                       {"role": "user", "content": response}])
 
         feedback = completion_feedback.choices[0].message.content.strip()
-        output["feedback"] = feedback
+        result.add_feedback("general", feedback=feedback)
 
-    return output
+    return result
diff --git a/app/evaluation_tests.py b/evaluation_function/evaluation_tests.py
similarity index 79%
rename from app/evaluation_tests.py
rename to evaluation_function/evaluation_tests.py
index 48b93b4..0db1a59 100755
--- a/app/evaluation_tests.py
+++ b/evaluation_function/evaluation_tests.py
@@ -26,8 +26,8 @@ def test_general_risk(self):
                       'main_prompt': "The student needs to enter a risk with a short description of how it can cause harm",
                       'feedback_prompt': feedback_prompt,
                       'default_prompt': default_prompt}
-        output = evaluation_function(response, answer, parameters)
-        self.assertEqual(output['is_correct'], True)
+        result = evaluation_function(response, answer, parameters).to_dict()
+        self.assertEqual(result.get("is_correct"), True)
 
     def test_photosynthesis_definition_correct(self):
         response = "Photosynthesis is the process by which plants convert light energy into chemical energy to fuel their growth."
@@ -35,8 +35,8 @@ def test_photosynthesis_definition_correct(self):
                       'main_prompt': "Evaluate the student's response for the definition of photosynthesis",
                       'feedback_prompt': feedback_prompt,
                       'default_prompt': default_prompt}
-        output = evaluation_function(response, answer, parameters)
-        self.assertEqual(output["is_correct"], True)
+        result = evaluation_function(response, answer, parameters).to_dict()
+        self.assertEqual(result.get("is_correct"), True)
 
     def test_photosynthesis_definition_incomplete(self):
         response = "Photosynthesis is the process by which plants make their food."
@@ -44,8 +44,8 @@ def test_photosynthesis_definition_incomplete(self):
                       'main_prompt': "Evaluate the student's response for the definition of photosynthesis. They should mention the conversion of light energy to chemical energy.",
                       'feedback_prompt': feedback_prompt,
                       'default_prompt': default_prompt}
-        output = evaluation_function(response, answer, parameters)
-        self.assertEqual(output["is_correct"], False)
+        result = evaluation_function(response, answer, parameters).to_dict()
+        self.assertEqual(result.get("is_correct"), False)
 
     def test_capital_city_incorrect(self):
         response = "The capital of France is Berlin."
@@ -53,8 +53,8 @@ def test_capital_city_incorrect(self):
                       'main_prompt': "Analyze the response regarding the capital of France",
                       'feedback_prompt': feedback_prompt,
                       'default_prompt': default_prompt}
-        output = evaluation_function(response, answer, parameters)
-        self.assertEqual(output["is_correct"], False)
+        result = evaluation_function(response, answer, parameters).to_dict()
+        self.assertEqual(result.get("is_correct"), False)
 
     def test_list(self):
         response = "Red, blue and yellow."
@@ -62,8 +62,8 @@ def test_list(self):
                       'main_prompt': "Mark this response asking students for the three primary colours in painting.",
                       'feedback_prompt': feedback_prompt,
                       'default_prompt': default_prompt}
-        output = evaluation_function(response, answer, parameters)
-        self.assertEqual(output["is_correct"], True)
+        result = evaluation_function(response, answer, parameters).to_dict()
+        self.assertEqual(result.get("is_correct"), True)
 
     def test_physics_definition(self):
         response = "The law of conservation of energy states that energy cannot be created or destroyed, only transformed from one form to another. It's a fundamental principle in physics."
@@ -71,8 +71,8 @@ def test_physics_definition(self):
                       'main_prompt': "Examine the explanation of the law of conservation of energy and provide feedback.",
                       'feedback_prompt': feedback_prompt,
                       'default_prompt': default_prompt}
-        output = evaluation_function(response, answer, parameters)
-        self.assertEqual(output["is_correct"], True)
+        result = evaluation_function(response, answer, parameters).to_dict()
+        self.assertEqual(result.get("is_correct"), True)
 
 
 if __name__ == "__main__":
diff --git a/evaluation_function/main.py b/evaluation_function/main.py
new file mode 100644
index 0000000..515a6b2
--- /dev/null
+++ b/evaluation_function/main.py
@@ -0,0 +1,23 @@
+
+from lf_toolkit import create_server, run
+
+from .evaluation import evaluation_function
+from .preview import preview_function
+
+def main():
+    """Run the IPC server with the evaluation and preview functions.
+    """
+
+    print("Creating Server")
+    server = create_server()
+
+    print("Setting Eval")
+    server.eval(evaluation_function)
+    print("seeting Preview")
+    server.preview(preview_function)
+
+    print("Running server")
+    run(server)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/evaluation_function/preview.py b/evaluation_function/preview.py
new file mode 100644
index 0000000..653bc3e
--- /dev/null
+++ b/evaluation_function/preview.py
@@ -0,0 +1,28 @@
+from typing import Any
+from lf_toolkit.preview import Result, Params, Preview
+
+def preview_function(response: Any, params: Params) -> Result:
+    """
+    Function used to preview a student response.
+    ---
+    The handler function passes three arguments to preview_function():
+
+    - `response` which are the answers provided by the student.
+    - `params` which are any extra parameters that may be useful,
+        e.g., error tolerances.
+
+    The output of this function is what is returned as the API response
+    and therefore must be JSON-encodable. It must also conform to the
+    response schema.
+
+    Any standard python library may be used, as well as any package
+    available on pip (provided it is added to requirements.txt).
+
+    The way you wish to structure you code (all in this function, or
+    split into many) is entirely up to you.
+    """
+
+    try:
+        return Result(preview=Preview(sympy=response, latex=response))
+    except Exception as e:
+        return Result(preview=Preview(feedback=str(e)))
\ No newline at end of file
diff --git a/evaluation_function/preview_test.py b/evaluation_function/preview_test.py
new file mode 100644
index 0000000..b0b4965
--- /dev/null
+++ b/evaluation_function/preview_test.py
@@ -0,0 +1,29 @@
+import unittest
+
+from .preview import Params, preview_function
+
+class TestPreviewFunction(unittest.TestCase):
+    """
+    TestCase Class used to test the algorithm.
+    ---
+    Tests are used here to check that the algorithm written
+    is working as it should.
+
+    It's best practice to write these tests first to get a
+    kind of 'specification' for how your algorithm should
+    work, and you should run these tests before committing
+    your code to AWS.
+
+    Read the docs on how to use unittest here:
+    https://docs.python.org/3/library/unittest.html
+
+    Use preview_function() to check your algorithm works
+    as it should.
+    """
+
+    def test_preview(self):
+        response, params = "A", Params()
+        result = preview_function(response, params)
+
+        self.assertIn("preview", result)
+        self.assertIsNotNone(result["preview"])
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..c0c41b2
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3124 @@
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
+    {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.2"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "aiohttp-3.13.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2372b15a5f62ed37789a6b383ff7344fc5b9f243999b0cd9b629d8bc5f5b4155"},
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+    {file = "zstandard-0.25.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:89c4b48479a43f820b749df49cd7ba2dbc2b1b78560ecb5ab52985574fd40b27"},
+    {file = "zstandard-0.25.0-cp39-cp39-win32.whl", hash = "sha256:1cd5da4d8e8ee0e88be976c294db744773459d51bb32f707a0f166e5ad5c8649"},
+    {file = "zstandard-0.25.0-cp39-cp39-win_amd64.whl", hash = "sha256:37daddd452c0ffb65da00620afb8e17abd4adaae6ce6310702841760c2c26860"},
+    {file = "zstandard-0.25.0.tar.gz", hash = "sha256:7713e1179d162cf5c7906da876ec2ccb9c3a9dcbdffef0cc7f70c3667a205f0b"},
+]
+
+[package.extras]
+cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and python_version < \"3.14\"", "cffi (>=2.0.0b) ; platform_python_implementation != \"PyPy\" and python_version >= \"3.14\""]
+
+[metadata]
+lock-version = "2.1"
+python-versions = "^3.11"
+content-hash = "527722f3dedd861ed16733ab031d828fd205e49a8ddca7aa84f7f4044942d15c"
diff --git a/poetry.toml b/poetry.toml
new file mode 100644
index 0000000..efa46ec
--- /dev/null
+++ b/poetry.toml
@@ -0,0 +1,2 @@
+[virtualenvs]
+in-project = true
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..9a09938
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,43 @@
+[tool.poetry]
+name = "evaluation_function"
+version = "0.0.1"
+description = ""
+authors = ["Marcus Messer <m.messer@imperial.ac.uk>"]
+readme = "README.md"
+packages = [{ include = "evaluation_function" }]
+
+[tool.poetry.scripts]
+evaluation_function = "evaluation_function.main:main"
+evaluation_function_dev = "evaluation_function.dev:dev"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+typing_extensions = "^4.12.2"
+lf_toolkit = { git = "https://github.com/lambda-feedback/toolkit-python.git", tag = "v1.0.1", extras = [
+    "ipc",
+] }
+aiohttp = "^3.13.2"
+aiosignal = "^1.4.0"
+attrs = "^25.4.0"
+certifi = "^2025.11.12"
+charset-normalizer = "^3.4.4"
+frozenlist = "^1.8.0"
+idna = "^3.11"
+iniconfig = "^2.3.0"
+multidict = "^6.7.0"
+openai = "^2.7.2"
+packaging = "^25.0"
+pluggy = "^1.6.0"
+python-dotenv = "^1.2.1"
+requests = "^2.32.5"
+tqdm = "^4.67.1"
+urllib3 = "^2.5.0"
+yarl = "^1.22.0"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.2.2"
+flake8 = "^7.1.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file