- Overview
- Topics Covered
- Getting Started
- Workflow Steps
- Project Structure
- AI Planning and Research
- Software Architecture
- Prompt Engineering
- Development Process
- Unreal Engine Integration
- Contributing
- License
The Think First Workflow Guide provides a structured approach to developing ambitious AI projects. By focusing on planning, research, and architecture upfront, this guide helps you streamline your workflow and enhance project outcomes.
Whether you are a beginner or an experienced developer, this guide will support you in effectively managing your AI projects.
This guide includes a variety of topics essential for successful project management in AI development:
- AI
- Development Process
- Gemini
- Guide
- LLM (Large Language Models)
- Project Management
- Prompt Engineering
- Roo Code
- Software Architecture
- Unreal Engine
- Workflow
To get started, visit the Releases section to download the latest version of the guide. Make sure to execute the downloaded file to access all features.
- Download the latest release from the Releases section.
- Extract the files to your preferred directory.
- Open the guide and follow the instructions to set up your project.
- Define Project Goals: Clearly outline what you want to achieve.
- Conduct Research: Gather information and resources related to your project.
- Architect the Solution: Plan the structure and components of your project.
- Implement: Start coding and integrating various elements.
- Test and Iterate: Regularly test your work and make improvements.
- Deploy: Launch your project and monitor its performance.
A well-organized project structure is crucial for success. Hereβs a recommended layout:
/project-root
βββ /docs
βββ /src
βββ /tests
βββ /assets
βββ README.md
- /docs: Documentation files.
- /src: Source code.
- /tests: Test scripts.
- /assets: Images, models, and other resources.
Before diving into coding, spend time on planning and research. Identify the tools and frameworks you will use. Understand the problem space and gather data that will inform your project.
- Define the problem statement.
- Identify your target audience.
- Research existing solutions and identify gaps.
Designing a solid architecture is essential for scalability and maintainability. Consider using established patterns like MVC (Model-View-Controller) or microservices, depending on your project needs.
- Use modular components.
- Ensure clear interfaces between modules.
- Document your architecture decisions.
In AI projects, prompt engineering plays a critical role, especially when working with language models. Craft effective prompts to guide AI behavior.
- Be specific about the desired output.
- Provide context to the AI.
- Test and refine prompts based on results.
Adopt a development process that suits your project. Agile methodologies can be beneficial for iterative development and feedback.
- Hold regular stand-ups to discuss progress.
- Use version control for code management.
- Review code regularly to maintain quality.
If your project involves game development, integrating with Unreal Engine can enhance your AI capabilities. Use the Unreal Engine API to implement AI features in your game.
- Install Unreal Engine from the official website.
- Create a new project and set up your environment.
- Integrate AI components as outlined in this guide.
We welcome contributions from the community. If you have suggestions or improvements, please submit a pull request. Ensure that your contributions align with the goals of this guide.
- Fork the repository.
- Create a new branch for your feature.
- Make your changes and commit them.
- Push your branch and create a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
For more information and updates, check the Releases section.