# What Is RAD? Why It Matters in the Age of AI Coding

## Executive summary

The methodology of Rapid Application Development (RAD), formalized in 1991, remains highly relevant for modern AI-assisted coding workflows. RAD emphasizes iterative development and user feedback across four phases: Requirements Planning, User Design, Construction, and Cutover. While AI agents can rapidly generate working prototypes from plain language prompts (effectively serving as the requirements document), the speaker cautions that deploying raw AI-generated code is risky due to potential security weaknesses (e.g., self-approval loopholes). The most robust approach involves integrating Spec Driven Development: using prototype discoveries to write a formal specification, which then becomes the basis for testing and production deployment.

## Key takeaways

- RAD Methodology Overview: RAD is an iterative methodology favoring speed and user feedback over detailed upfront planning (the waterfall approach). It consists of four phases: Requirements Planning, User Design (prototyping), Construction (short cycles with continuous testing), and Cutover (deployment/migration).
- AI Agents Map to RAD Phases: The modern process of using AI agents maps well onto RAD: the initial prompt serves as lightweight requirements planning; the agent generates a clickable prototype for user design; construction involves continuous code generation (data schema, workflow logic); and cutover is deployment.
- The Importance of Spec Driven Development: To mitigate security risks inherent in AI-generated code (studies suggest up to 45% carry weaknesses), the process must transition from relying solely on the prototype to formalizing discoveries into a written specification. This spec becomes the verifiable source of truth for production.

## Technical details

- RAD Phases: 1. Requirements Planning (lightweight definition of problem, users, features, and constraints). 2. User Design (building a clickable prototype for early user feedback). 3. Construction (building functionality in short cycles with continuous testing/feedback). 4. Cutover (deployment to production, data migration, and user training).
- AI Prompting as Requirements: In modern AI development, the initial plain language prompt sent to an agent acts as the 'requirements document,' defining scope (e.g., who the users are and what the approval rules are).
- AI Code Generation Risks: Relying solely on AI-generated prototypes for production is risky, as agents may fail to implement critical business rules (e.g., allowing an employee to approve their own expenses). Studies indicate that approximately 45% of AI code samples contain some type of security weakness.
- Spec Driven Development: This process dictates that discoveries from the prototype are written down as a formal specification (business rules, acceptance criteria, security requirements). This spec then becomes the mandatory test suite for the generated code before production deployment.

## Practical implications

- When integrating AI agents into development pipelines, treat the initial prompt as a draft specification, not the final requirements document.
- Implement mandatory security and business rule verification steps (e.g., second sign-off logic) that are captured in a formal spec before deployment.
- Adopt an iterative build process where prototypes inform a detailed specification, which then drives unit and integration testing for production code.

## Topics

Rapid Application Development (RAD), AI Agents, Prompt Engineering, Spec Driven Development, Software Architecture, Security Vulnerability Mitigation, Application Development Without Programmers, IBM Technology Newsletter

Source: https://www.youtube.com/watch?v=J0zbWsutyA8