#3776 Checklist for AI Auto-Code
#3776 Checklist for AI Auto-Code
#3776 What are the 10 major elements needed to describe an interactive web application to an AI auto-coder?
Of course. To effectively describe an interactive web application to an AI auto-coder, you need to provide a clear, structured, and comprehensive blueprint. Think of it as creating the ultimate requirements document for a developer who is incredibly fast but has zero intuition.
Here are the 10 major elements needed to describe an interactive web application to an AI auto-coder:
1. High-Level Concept & Goal
This is the mission statement. It defines the "why" behind the application in a single, concise paragraph. The AI needs to understand the core purpose to make logical inferences when details are ambiguous.
What to include: The primary problem the app solves, the target audience, and the key value it provides.
Example: "This is a project management tool for small marketing teams. It helps them track tasks, deadlines, and client feedback in one place to prevent miscommunication and ensure projects are delivered on time."
2. User Roles & Permissions
Define every type of user who will interact with the system. This is crucial for security and feature access.
What to include: A list of roles (e.g.,
Admin,Project Manager,Team Member,Client) and a breakdown of what each role can and cannot do (e.g., "OnlyAdminscan delete projects," "Clients can only view and comment on tasks they are assigned to").Example:
Admin: Full CRUD (Create, Read, Update, Delete) access on all data. Can manage users.Project Manager: CRUD access on projects and tasks they create. Can assign tasks toTeam Members. Read-only access to other projects.Client: Read and Comment access only on projects they are invited to.
3. Core Features & User Stories
This is the heart of the application—the "what" it does. The best format for this is user stories, which are short, descriptive sentences from a user's perspective. They are easy for an AI to parse into functional requirements.
What to include: A prioritized list of features in the format: "As a [user role], I want to [perform an action], so that I can [achieve a goal]."
Example: "As a
Project Manager, I want to create a new project and set a deadline, so that I can start assigning tasks to my team."
4. UI/UX Wireframes & Design System
While you may not provide a pixel-perfect design, you must describe the layout, user flow, and visual rules. This tells the AI how the application should look and feel.
What to include:
Page/View Descriptions: A list of all screens (e.g., Dashboard, Project View, Settings Page) and the components on each.
User Flow: How a user navigates from one screen to another to complete a task (e.g., "User clicks 'Login' -> enters credentials -> is redirected to Dashboard").
Design System: Basic rules for colors, fonts, button styles, and spacing (e.g., "Primary color is
#4A90E2", "All buttons should have a4pxborder-radius").
5. Data Models & Schema
This is the application's brain. You must define the structure of the data it will store. This is the blueprint for the database.
What to include: A list of data objects, their properties (fields), data types, and the relationships between them.
Example:
User:id(integer, primary key),email(string, unique),password_hash(string),role(string).Project:id(integer, primary key),name(string),due_date(datetime),owner_id(foreign key toUser.id).Relationship: A
Usercan have manyProjects.
6. Backend Logic & API Endpoints
This describes the server-side operations. How does the frontend talk to the backend? Define the specific communication channels (API endpoints) and what happens when they are called.
What to include: A list of API endpoints, the HTTP method (GET, POST, PUT, DELETE), the expected input (request body/parameters), and the expected output (response).
Example:
POST /api/projects: Creates a new project. Expects{name: "New Project", due_date: "2025-12-31"}. Returns the created project object.GET /api/projects/{id}: Retrieves a single project by its ID. Returns the project object or a 404 error if not found.
7. Authentication & Security Requirements
Specify exactly how users will prove their identity and how data will be protected.
What to include: The authentication method (e.g., email/password with JWT tokens, OAuth 2.0 with Google/GitHub), password policies (e.g., minimum length, complexity), and any other security considerations (e.g., "All user input must be sanitized to prevent XSS attacks").
8. Third-Party Integrations
List any external services the application needs to connect to. Modern applications rarely exist in a vacuum.
What to include: The service name (e.g., Stripe for payments, SendGrid for emails, Google Maps for location), the purpose of the integration, and any API keys or credentials needed (to be stored securely).
Example: "Integrate with the Stripe API to process monthly subscription payments. Use SendGrid for transactional emails like password resets and welcome messages."
9. Error Handling & Validation Logic
A robust application knows how to handle things when they go wrong. You must define the rules for valid data and the messages to show when those rules are broken.
What to include:
Input Validation: Rules for forms (e.g., "Email field must be a valid email format," "Password must be at least 8 characters long").
Error Messages: Specific, user-friendly messages for different errors (e.g., "This email address is already in use. Please log in or use a different email.").
System States: What to show during loading, on success, or when an empty state occurs (e.g., "No projects found. Create your first one!").
10. Technology Stack & Deployment Environment
Tell the AI what tools to use and where the application will live. This ensures the generated code is in the correct language and framework and is configured for the right environment.
What to include:
Frontend: Framework (e.g., React, Vue, Svelte).
Backend: Language/Framework (e.g., Python/Django, Node.js/Express).
Database: System (e.g., PostgreSQL, MongoDB, SQLite).
Deployment: Target platform (e.g., Vercel, AWS, Google Cloud) and any environment variables required.
Given your background in software process engineering, you can think of these 10 elements as a comprehensive Software Requirements Specification (SRS) tailored specifically for a generative AI agent. The more precise and unambiguous you are with each element, the higher the quality of the generated code will be.
Create a checklist for the user to complete
Of course. Here is a checklist based on the 10 major elements. You can use this to structure your requirements for any interactive web application project you're planning.
Web Application Specification Checklist for AI Auto-Coders
Use this checklist to ensure you have provided a comprehensive and unambiguous specification for your interactive web application.
☐ 1. High-Level Concept & Goal
[ ]Mission Statement: Write a 1-2 paragraph description of the application's core purpose, target audience, and the primary problem it solves.
☐ 2. User Roles & Permissions
[ ]List Roles: Identify all distinct user roles (e.g.,Admin,Editor,Viewer,Guest).[ ]Define Permissions: For each role, create a table or list defining what actions they can perform (e.g., Create, Read, Update, Delete) on which data models.[ ]Document Restrictions: Note any specific access restrictions (e.g., "Editors can only modify content they created").
☐ 3. Core Features & User Stories
[ ]List Major Features: Document all high-level features of the application (e.g., User Authentication, Project Dashboard, Reporting).[ ]Write User Stories: For each feature, write specific user stories in the format: "As a[user role], I want[to perform an action], so that[I can achieve a goal]."[ ]Define Acceptance Criteria: For each user story, list the specific conditions that must be met for the feature to be considered complete.
☐ 4. UI/UX Wireframes & Design System
[ ]List All Views: Identify every unique page or screen in the application.[ ]Describe View Components: For each view, briefly describe its layout and the key UI components it contains (e.g., navigation bar, data table, forms).[ ]Map User Flows: Diagram or describe the step-by-step navigation for at least 3-5 primary user tasks (e.g., Registration Flow, New Item Creation Flow).[ ]Specify Design System:[ ]Primary, Secondary, and Accent colors (with hex codes).[ ]Font family and base font size.[ ]Style for primary and secondary buttons.
☐ 5. Data Models & Schema
[ ]Identify Data Objects: List all primary data objects (e.g.,User,Product,Order).[ ]Define Fields: For each object, list all fields with their data types (string,integer,boolean,datetime, etc.).[ ]Add Constraints: Specify field constraints likeunique,required,default value, or character limits.[ ]Map Relationships: Define the relationships between objects (one-to-one,one-to-many,many-to-many).
☐ 6. Backend Logic & API Endpoints
[ ]List API Endpoints: Create a list of all necessary API endpoints (e.g.,GET /api/users,POST /api/users).[ ]Specify Methods & Payloads: For each endpoint, define the HTTP method, the expected request body/parameters, and the structure of the success/error JSON response.
☐ 7. Authentication & Security Requirements
[ ]Choose Strategy: Specify the authentication method (e.g., JWT with email/password, OAuth 2.0 with Google).[ ]Define Password Policy: Document rules for password strength (minimum length, character types).[ ]List Security Measures: Note other required security practices (e.g., all inputs must be sanitized, use HTTPS, set CORS policy).
☐ 8. Third-Party Integrations
[ ]List Services: Identify every external service the app will use (e.g., Stripe, SendGrid, Google Maps).[ ]State Purpose: For each service, describe its function within the application.[ ]Note Credentials: List the API keys/credentials that will be needed (and remind yourself to store them securely as environment variables).
☐ 9. Error Handling & Validation Logic
[ ]Define Validation Rules: For every user input field, specify its validation rules.[ ]Write User-Facing Messages: Document the exact text for common error messages.[ ]Describe UI States: Specify what the UI should display forloading,empty, andsuccessstates for key interactions.
☐ 10. Technology Stack & Deployment Environment
[ ]Frontend Stack: Specify the framework (e.g., React, Vue).[ ]Backend Stack: Specify the language and framework (e.g., Node.js/Express, Python/Django).[ ]Database System: Specify the database (e.g., PostgreSQL, MongoDB).[ ]Deployment Target: Name the intended hosting platform (e.g., Vercel, AWS Amplify, DigitalOcean).[ ]List Environment Variables: List all environment variables the application will need to run (e.g.,DATABASE_URL,API_KEY).
Completing this checklist will create a robust blueprint for the AI, minimizing ambiguity and maximizing the chances of a successful code generation.
Repeat for a Jupyter Notebook AI application
Of course. Here's a checklist adapted for specifying a Jupyter Notebook AI application to an AI auto-coder.
Jupyter Notebook AI Application Checklist
Use this checklist to define the requirements for an AI-powered data analysis, model training, or experimental notebook.
☐ 1. Objective & High-Level Goal
[ ]Problem Statement: Clearly define the primary research question or business problem the notebook will address.[ ]Expected Deliverable: State the final output (e.g., a trained model file, a CSV with predictions, a PDF report with key findings, a set of visualizations).
☐ 2. Audience & Use Case
[ ]Primary User: Identify who will be running or reading the notebook (e.g., data scientist, machine learning engineer, business analyst).[ ]Intended Use: Describe how the notebook will be used (e.g., one-off exploratory data analysis, a reusable model training script, an automated weekly report generator).
☐ 3. Analytical Steps & Key Objectives
[ ]Workflow Outline: List the major sections of the notebook in logical order (e.g., 1. Data Ingestion, 2. Data Cleaning, 3. Exploratory Data Analysis (EDA), 4. Feature Engineering, 5. Model Training, 6. Model Evaluation, 7. Conclusion).[ ]Section Goals: For each section, state the specific objective (e.g., "In EDA, we will identify the top 5 most correlated features with the target variable").
☐ 4. Notebook Structure & Visualization Strategy
[ ]Narrative Flow: Describe how Markdown cells will be used to explain the code, interpret results, and guide the reader through the analysis.[ ]Key Visualizations: List the specific plots, charts, and tables required and what insight each should provide (e.g., "A confusion matrix to show model accuracy," "A histogram of customer age distribution").[ ]Styling: Specify any desired styling for outputs (e.g., "Use theseabornlibrary with the 'whitegrid' style for all plots").
☐ 5. Data Sources, Schema & Outputs
[ ]Input Data: List all data sources, including file paths (e.g.,data/raw/sales.csv), database connection strings, or API endpoints.[ ]Input Schema: Define the expected columns and data types of the primary input dataframes.[ ]Output Specification: Describe the format, file name, and schema of any data or models saved by the notebook (e.g., "Output a file namedpredictions.csvwithcustomer_idandpredicted_churn_scorecolumns").
☐ 6. Core Logic, Algorithms & Models
[ ]Algorithms: Specify the key algorithms to be used in the analysis (e.g., TF-IDF for text, K-Means for clustering, Logistic Regression for classification).[ ]Custom Functions: Outline any critical, reusable functions or classes that should be created within the notebook (e.g., a functionnormalize_text(text)or a classFeatureCreator).
☐ 7. Data Security & Dependency Management
[ ]Credentials Handling: Specify how sensitive information like API keys or database passwords will be managed (e.g., loaded from environment variables, using a.envfile).[ ]Dependency File: State the plan for listing required libraries (e.g., "Create arequirements.txtfile").
☐ 8. External Data & API Integrations
[ ]List External Services: Identify any external APIs the notebook needs to call for data enrichment or other purposes.[ ]Endpoint & Payload: For each API, specify the endpoint to use and the structure of the data you expect in return.
☐ 9. Error Handling & Data Validation
[ ]Input Validation: Define checks to be performed on the raw data (e.g., "Check for and report the percentage of missing values in each column").[ ]Assertions: List key points in the notebook where assertions should be used to validate the state of the data (e.g.,assert that the dataframe is not empty after filtering).[ ]Error Logging: Describe how the notebook should handle potential errors, such as a failed API call or a model that fails to converge.
☐ 10. Environment & Dependencies Specification
[ ]Python Version: Specify the required Python version (e.g.,Python 3.10).[ ]Required Libraries: List all necessary Python libraries and their specific versions (e.g.,pandas==2.1.1,scikit-learn==1.3.1).[ ]Hardware Requirements: Note any special hardware needs (e.g., "Requires a GPU with >= 8GB VRAM," "Requires at least 16GB of RAM").
From <https://gemini.google.com/app/> Google Gemini 2.5 Pro
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