AI-Assisted Software Development with Language Models

In this blog post, we'll explore how to best use Language Models to generate code and create full software applications. We will break down the process into stages and steps, allowing you to harness the power of AI and build your app efficiently.

Overview of the Process

1. Get a detailed explanation of the steps to build the app.
2. Break down the development process into further detail.
3. Describe all the files involved in the project, preferably in a table.
4. Prompt the Language Model for a detailed description of each file.
5. Feed the detailed descriptions to a Language Model Codex that writes code.

Stage 1: Explain Steps to Build the App

The first step is to ask the Language Model to provide a high-level explanation of the steps required to build the app. This will give you a solid foundation and understanding of what is needed in terms of planning, design, development, and deployment.

Stage 2: Break Down Development Process

After you have a high-level understanding of the steps, ask the Language Model to break down each step into further detail. This will help you understand the specific tasks involved in each step, ensuring that you don't miss any crucial elements in the development process.

Stage 3: Describe Files in a Table

Next, request that the Language Model describe all the files involved in the project. Having this information organized in a table makes it easy to keep track of each file's purpose and how it contributes to the overall application.

Stage 4: Prompt for a Detailed Description of Each File

Once you have a clear understanding of each file's purpose, prompt the Language Model to provide a detailed description of each file. This will give you a deeper understanding of the file structure, dependencies, and the specific functions or features implemented in each file.

Stage 5: Generate Code with LLM Codex Model

Finally, once you have gathered all the necessary information, feed the detailed descriptions of each file to an LLM Codex model that writes code. This will generate the actual code for your application based on the specifications provided by the Language Model.

By following this methodology, you can leverage Language Models to create software applications efficiently and effectively. As AI continues to advance, the process of software development will become more streamlined and accessible to a wider audience.

Conclusion

In this blog post, we explored how to best use Language Models to generate code and create full software applications. We broke down the process into stages and steps, allowing you to harness the power of AI and build your app efficiently.

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Thanks for reading! Here is a UML for your consumption:

The Plannerai IDE

Using the above UML we were able to describe an application that can follow this workflow using just a starting prompt from the user. Every step thereafter is generated by storing the responses in a data structure that represents the new application that is going to be developed.

The generated response will be sent back to another model with some prepending of prompt text containing instructions on how to use the following data. The response is stored again in a data structure that can be referenced back for every step. The new responses are then sent to specifically fine-tuned Davinci models that return specific outputs, such as taking a description and turning it into a list of steps. Those steps are then broken up into an array and stored as individual items. The steps are iterated over and prompt text is prepended and appended to the step and sent to a codex llm which turns the step item into code.

Detailed breakdown of each step:

1. User -> LM: Identify app requirements and goals: The user provides a description of the application they have in mind and the LM generates a list of goals and requirements based on the user's description.
2. User -> LM: Determine app architecture: The user provides a description of the architecture and the LM generates a list of the components and their interactions.
3. User -> LM: Create user interface design: The user describes their desired user interface and the LM generates a list of commands, layouts, and styles.
4. User -> LM: Plan data structure and algorithms: The user provides a description of the data structure and algorithms that are used in the application and the LM generates a list of commands and operations.
5. User -> LM: Plan development environment: The user provides a description of the development environment and the LM generates a list of commands and operations needed to set up the environment.
6. User -> LM: Describe Dev environment setup scripts: The user provides a description of the setup scripts and the LM generates a list of commands and instructions needed to execute them.
7. User -> LM: Test and debug code: The user provides a description of the testing and debugging process and the LM generates a list of commands and operations needed to execute them.
8. User -> LM: Describe deployment from CI to CD: The user provides a description of the deployment process and the LM generates a list of commands and operations needed to execute them.
9. User -> LM: Choose appropriate hosting and deployment platform: The user chooses an appropriate hosting and deployment platform and the LM generates a list of commands and operations needed to execute them.
10. User -> LM: Describe deployment code in detail: The user provides a detailed description of the deployment code and the LM generates a list of commands and operations needed to execute it.
11. User -> LM: Describe app performance and functionality monitoring: The user provides a description of the performance and functionality monitoring process and the LM generates a list of commands and operations needed to execute it.
12. User -> LM: Create table listing all project files: The user provides a description of all the project files and the LM generates a table listing the files, their purposes, dependencies, inputs/outputs, file sizes, and locations.
13. User -> LM: Identify purpose of each file: The user provides a description of the purpose of each file and the LM generates a list of commands and operations needed to execute them.
14. User -> LM: Note any dependencies and inputs/outputs: The user provides a description of the dependencies and inputs/outputs of each file and the LM generates a list of commands and operations needed to execute them.
15. User -> LM: Include file size and location: The user provides a description of the file size and location and the LM generates a list of commands and operations needed to execute them.
16. User -> LM: Request detailed description of each file: The user requests a detailed description of each file and the LM generates a list of commands and operations needed to execute them.
17. User -> LM: Provide file structure, function, dependencies, and interactions: The user provides a description of the file structure, function, dependencies and interactions and the LM generates a list of commands and operations needed to execute them.
18. User -> LM: Record file description for future reference: The user provides a description of the file for future reference and the LM generates a list of commands and operations needed to execute them.
19. User -> Codex: Feed detailed descriptions to LLMCodex model: The user feeds the detailed descriptions to the LLMCodex model and the Codex generates code based on the provided specifications.
20. Codex -> LM: Generates code based on provided specifications: The Codex generates code based on the provided specifications and the LM provides specifications for the code.
21. LM -> Codex: Provides specifications: The LM provides specifications for the code and the Codex generates code for the software application.