GPT Engineer is intended to create software based on your specifications. Simply provide a prompt, and GPT Engineer will ask clarification as needed before generating a whole codebase customized to your chosen coding style and functionality. It is adaptable, extensible, and allows you to train your agent to understand your coding preferences.
In this article, I’ll show you How to Use next generation of AI-powered development tools of GPT Engineer step by step procedures. I hope at the end of this article you will get the complete tutorial of the GPT engineer.
Please complete the following steps to prepare your environment for using GPT Engineer:
Follow these steps to clone the GPT Engineer repository:
- Launch the command prompt or terminal.
- Navigate to the repository’s desired cloning directory.
- To clone the repository, use the following command:
git clone <repository_url>
To get to the project directory after cloning the GPT Engineer repository, do the following:
- Launch the command prompt or terminal.
- Use the cd command, followed by the cloned repository’s directory path. As an example:
Run the following command to create a new Conda environment:
conda create -name gpt-eng python=3.11.3
Activate the Conda Environment using following command:
For Windows users,
conda activate gpt-eng
source activate gpt-eng
Make sure you are in the project directory, which contains the requirements.txt file. To get to the project directory, use the cd command.
To install the prerequisites, use the following command:
pip install -r requirements.txt
Set Up OpenAI API Key
Set the API key as an environment variable once you’ve obtained it:
To get the OpenAI API key, follow these step-by-step instructions:
- Visit the OpenAI website (https://openai.com) and create an account if you don’t have one already.
- Once you’re logged in, go to your account overview page. You can usually find a link to it in the top navigation menu or by clicking on your account profile.
- On the account overview page, locate the section related to the OpenAI API. It may be labeled as “API” or “API Keys.”
- Click on the link or button that takes you to the API Keys view. This will open a page displaying your API keys.
- In the API Keys view, look for an option to create a new secret key. It may be labeled as “Create new key” or something similar.
- Click on the button to create a new secret key. This will generate a new API key for you.
- Optionally, you may be prompted to provide a name for the API key. Enter a descriptive name to help you identify its purpose later on.
- Once the key is generated, make sure to copy and securely store it. You won’t be able to retrieve it again from the web interface, so it’s crucial to keep a record of it for future use.
Remember to treat your API key as sensitive information and keep it secure.
For Windows (Command Prompt):
For Windows (PowerShell):
Ready to Use:
You have now successfully configured the environment for utilizing GPT Engineer. You can begin using the tool by executing the required scripts or incorporating the code into your applications.
Steps to run GPT Engineer
Please follow these steps to run GPT Engineer with the provided instructions:
Make a New Empty Folder:
Make a new folder at the location you want. This can be done manually or via the command line. To create a new folder named “my-new-project” in the current directory, for example, type:
Copy Example Files (Optional):
To begin with an example project structure, copy the contents of the “example” folder into your newly created folder. To copy the files and folders, use the following command:
cp -r example/* my-new-project/
Fill in the Main Prompt:
Open a text editor and navigate to the “main_prompt” file in your “my-new-project” folder. Replace the present content with the code generating prompt of your choice. Make certain that your prompt properly specifies the necessary functionality or code structure.
Run the GPT Engineer Script:
Navigate to the GPT Engineer root directory (the folder containing “main.py”).
To run GPT Engineer and produce code depending on your main prompt, enter the following command:
python main.py my-new-project
This command tells GPT Engineer to process the main prompt in the “my-new-project” folder and create code.
Process the Main Prompt
Here are some details to clarify:
Summary of areas that need clarification: 1. Details about the snake game (rules, features, etc.) 2. Specifics about the MVC components (Model, View, Controller) 3. How to handle keyboard control in Python 4. File organization and structure Clarifying question: Could you please provide more details about the snake game, such as the rules, features, and any additional requirements (answer in text, or “q” to move on)
Rules of the Snake Game for Implementation:
The Snake game is a famous arcade-style game that an engineer can construct by following these rules: Grid of the Game: The game takes place on a two-dimensional grid or screen that has been divided into cells. The grid’s size can be predefined or customizable depending on the game’s settings. Snake Motion: On the grid, the snake is represented as a series of connected segments or blocks. The snake begins with a single segment and goes in a single direction. The player can control the direction by utilizing keyboard inputs (e.g., arrow keys). The snake advances in the direction it is now facing, one cell at a time. Food items include: Food items are put on the grid at random. The goal of the game is for the snake to devour the food items in order to grow longer. When the snake’s head collides with a food item, it consumes it and grows longer. Snake Development: The snake grows longer by adding a new segment to its body as it consumes food. The newly acquired segment is attached to the snake’s tail. Detection of Collisions: To determine the game’s outcome, the game should detect collisions between the snake and various items. Self-Collision: The game is over if the snake’s head collides with any section of its own body. Wall Collision: If the snake’s head collides with the grid’s boundaries, the game is over. Food Collision: When the snake’s head collides with a food item, it consumes the food, grows longer, and its score rises. Game Over: When the game ends due to collision with the snake’s body or walls, a Game Over condition is triggered. The final score is displayed, indicating the number of food items consumed. The player has the option to play again or quit the game.
These rules serve as the foundation for developing the Snake game. These principles can be used by the engineer to build the game’s logic and implement the functions required for snake movement, collision detection, food generation, score tracking, and game over situations.
They can also include elements like stages, speed variations, and graphical interfaces to improve the gameplay experience.
Features of GPT Engineer
- Identification: The identity of the AI agent can be specified by editing the files in the identity folder. This enables users to tailor the AI agent to their own requirements. Users can, for example, specify the AI agent’s name, gender, and personality qualities.
- Memory: The AI agent can remember stuff between projects by altering the identity and evolving the main_prompt. As a result, the AI agent can learn and develop over time.
- Communication history: The communication history with GPT4 for each step in steps.py will be saved in the logs folder. This enables users to monitor the AI agent’s progress and troubleshoot issues.Scripts/rerun_edited_message_logs.py can be used to redo the communication history.
I think these are all great user features. They allow users to customize the AI agent, track its progress, and experiment with different approaches. This will make the AI agent more useful and effective for a wider range of people.
Also read: You might also find useful our guide on Basic Prompt Engineering Guide
Impact of GPT-Engineer
The introduction of GPT-Engineer has had a tremendous impact on different fields by utilizing the strength of GPT models. One of its outstanding skills is the ability to generate code in seconds using only a few words as input. This has substantially accelerated the development process and decreased the time and effort necessary for coding activities.
Furthermore, GPT-Engineer offers comprehensive customization possibilities, allowing customers to alter the AI agent’s behavior and memory across several projects. This can be accomplished by modifying or adding files to the identity folder, allowing users to specify the AI agent’s individual features and expertise.
Furthermore, the GPT-Engineer code generating process is visible and traceable. Every stage of the code generation process is documented and saved in the logs folder. This functionality allows users to go back and rerun certain phases, allowing for iterative refinement and debugging of generated code. It encourages an efficient workflow and improves the capacity to fine-tune and improve output based on the necessary requirements.
Overall, the advent of GPT-Engineer has revolutionized code production by using the capabilities of GPT models, enabling rapid and efficient coding, advanced customisation, and iterative code refining. It has enormous potential for reducing development processes and boosting innovation across multiple areas.
In conclusion, GPT Engineer represents the next generation of AI-powered development tools. With its ability to generate entire codebases based on prompts and its flexible and adaptable nature, it simplifies the process of code generation and customization. From high-level prompting to seamless human-AI interaction, GPT Engineer empowers developers to efficiently build and extend their projects, opening up new possibilities in the realm of AI-driven software development. Please feel free to share your thoughts and feedback in the comment section below.