As a highly advanced language model, ChatGPT can be fine-tuned on specific tasks, or on specific data, to improve its accuracy and performance even more. It can be fine-tuned on a dataset of Linux commands and corresponding outputs, or on a dataset of Helm commands and corresponding outputs, to simulate a Linux terminal or Helm playground respectively.

Here’s how I turned ChatGPT into a Kubernetes and Helm Playground.

Ajeet: Hello GPT. Good Morning. I want you to act as a Mac terminal. Kubernetes is already installed. I will type some commands and you’ll reply with what the terminal should show. Please show the result using the Mac terminal. When I tell you something, I will do so by putting text inside curly brackets {like this}. Please don’t write an explanation just the command result is sufficient. My first command is the kubectl version.

Ajeet: {kubectl get po,deploy,svc}

Ajeet: {kubectl run –image=nginx nginx-app –port=80 –env=”DOMAIN=cluster”}

Ajeet:{kubectl expose deployment nginx-app –port=80 –name=nginx-http}

Ajeet: {kubectl get po,svc,deploy}

Ajeet: {curl 10.100.67.94:80}

Ajeet: {helm repo add bitnami https://charts.bitnami.com/bitnami}

Ajeet: {helm repo update}

Ajeet: {helm install bitnami/mysql –generate-name}

Ajeet: {helm show chart bitnami/mysql}

Additional Resources:

• How to Integrate ChatGPT to a Discord Server and Run as a ChatBot
• Turning ChatGPT into Docker Playground in 5 Minutes
• Running ChatGPT Client locally on Kubernetes Cluster using Docker Desktop
• Running ChatGPT Client Locally using Docker Desktop
• Using ChatGPT to Build an Optimised Docker Image using Docker Multi-Stage Build
• Can ChatGPT Debug and Fix all of your Docker and Kubernetes Issues?