**"AI First Development"** is a shift in software development methodology, focusing on using AI to generate code and create functionality automatically. Here's how to do AI-first development. **TLDR**: Write out the requirements. Work from a specs doc. Create the API. Define the API surface. Generate the file and code as much as possible. ### Change the Developer Mindset Traditionally, developers have been accustomed to meticulously writing each line of code, solving every problem manually, and deeply involving themselves in the fine-grained details of implementation. With AI-first principles, the focus transitions from manual coding to defining requirements, designing the architecture, and specifying high-level functionality. The mindset shifts from "How do I implement this feature?" to "How do I best describe this feature so AI can implement it?" Developers shift their perspective to view their app as a series of building blocks, integrations, and connections that interact seamlessly with AI. They focus on designing modular components and clearly defined interfaces that AI can help implement, understanding that their role is now to provide the context and structure for AI to generate efficient, scalable solutions. ### Example Imagine you are developing a backend API for a new application. Instead of manually writing each piece of code, you start by defining the requirements and API surface. Once this is done, you let an AI tool generate the actual code for the API, including all the business logic and underlying functionality. - **Old:** Writing each part of the Python code manually. - **New:** Write design specs, define API signatures, and let AI generate the connections and logic between components. ### Workflow #### 1. Defining Specifications and Use Cases - Instead of writing code from scratch, begin by clearly outlining: - **Design Specs**: Describe what the application needs to do at a high level. - **Context Loading**: Feed the AI files and data. - **Functionality**: Summarize the functionality of each file and component at the top of the file. - **APIs**: Define the endpoints, input/output, and interactions. #### 2. Generating Code and Functionality with AI - Use AI to generate code based on the provided specs. This includes: - **API logic** - **Function definitions** - **Connecting business logic and data flows** ### Making Code AI Accessible ChatGPT's function calling feature is a good example of AI-First Development. When using libraries, the code must be structured and written in a way that facilitates easy referencing and integration—functions, models, and variables must be defined clearly and made accessible from a central location. Similarly, with AI-first principles, developers need to write well-defined function signatures, data models, and modular components that AI can use to generate code automatically. The key is to provide context in a structured form that allows AI to understand, reference, and execute code. ### Key Principles 1. **Automation Over Manual Work** - Developers focus more on defining the architecture, functionality, and expected outcomes while allowing AI to handle the boilerplate and low-level code. 2. **Design and Specification-Driven Development** - Developers focus on designing high-level specifications, including API definitions, use cases, and business logic. These guide AI in generating underlying code. 3. **Iterative Prototyping and Deployment** - Prototyping becomes fast and iterative, as AI can quickly generate, refine, and optimize code based on the developer’s specifications. Tools like Google Colab and GitHub are used to sync and deploy changes. 4. **Structured Prompts for Context** - Developers create structured prompts to help AI understand the context of the features being implemented, allowing the AI to autonomously generate UI, backend logic, and other components. 5. **AI-Assisted Documentation** - AI generates tutorials, SDK guides, and reference materials based on code and existing documents, reducing the need for manual documentation. ### Examples #### Documentation Generation - **Old:** Writing documentation manually for SDK. - **New:** Pass the SDK, codebase, and any previous documentation into the AI, allowing it to generate updated, accurate, and complete documentation for developers. #### UI Generation - **Old:** Use freeform drag-and-drop editors manually. - **New:** Define API structures for the editor, use structured prompts to explain what each feature does, and allow AI to generate the UI layout from a text description. #### Maintaining SDKs - **Old:** Manually maintain SDK updates and bug fixes. - **New:** Define the use cases for the SDK, generate API definitions, and allow AI to generate the function logic, reducing the need for manual intervention in each iteration.