What Is RAD? Why It Matters in the Age of AI Coding Transcript, AI Summary & Key Points

00:00 Back in 1982, a computer scientist named James Martin published a book with a title that might sound oddly familiar. Application Development Without Programmers. Martin formalized that idea in 1991 into a software development methodology called Rapid Application Development or RAD. Well, application development... Without programmers is something that's very much happening today with agentic AI.

00:34 And it turns out the RAD methodology from back in the day can teach us a thing or two about the best way to code with AI agents today. But first let's define RAD as it was all those years ago. So RAD's a software development methodology that favors speed and iterative development and user feedback. Over the waterfall approach that basically everybody was using at the time, which includes a lot of detailed upfront planning.

01:04 Now, RAD runs in four phases. Phase number one is requirements planning, which in RAD is pretty lightweight. Requirements planning basically means you define some things, like you define the problem that you're gonna solve and the users that this application is going to affect. We'd also want to add in whatever features that we want to include and also any constraints that we need to be concerned about.

01:37 But that's about it. And that's because RAD's founding assumption is that users don't really know what they want until they can see something and react to it. So let's phase one. Phase two of RAD is user design. So a team quickly builds a clickable prototype. This is something that users can actually use. And they put this in front of real life users with the idea being that any bad assumptions are gonna get caught early while they're still relatively cheap to fix.

02:19 And in RAD, that prototype... Is very much a keeper. We're gonna keep this guy around. The team keeps building on it until it grows to become the actual product itself. Then phase number three, that's Construction where the actual application functionality gets built and that happens in short cycles with testing and feedback happening continuously rather than waiting until the end and then in phase four that's cut over, which means deployment to production, migration of data and training the users how to use this new

02:53 system. Now Martin's book talked about small teams delivering working systems in time boxes of roughly 90 days, which at the time was extremely fast in an era where a sort of typical corporate software project probably ran for years. So right here it describes a methodology with fast delivery where users get to influence the design while it's still cheap enough to make changes to it.

03:31 Sounds pretty good right, and yet if I'd asked a developer a decade ago what methodology their team followed probably wouldn't have been RAD. RAD didn't really stick around because the code generators at the time, the computer-aided software engineering tools of RAD's era couldn't really build very sophisticated applications, but building a working application from plain language is, well, now very much possible.

03:58 Today, I can describe an app to an agentic AI coding tool and watch a version appear in minutes. I am talking, of course about good old vibe coding. So prototyping, the thing that RAD built its entire methodology around, has become super fast and that I think is why RAD's old four-phase methodology maps so well onto AI development. So let me show you what I mean with a really exciting example that vibe coders the world over just can't stop making.

04:33 That is, of course, an expense approval app. Oh yes, why pay for a real one when you can vibe code one yourself? So how does the first rad stage of requirements planning actually come in here? Well basically it is a prompt that somebody writes and sends that prompt to an AI agent in order to actually build the application. So maybe somebody on the finance team in this case describes the problem in plain language.

05:07 That's the prompt. Like who the users are, and what the approval rules are, which is you'll notice exactly the lightweight planning that RAD calls for in this phase. The prompt has effectively become the requirements document. Now for user design we take this AI agent which we have prompted and what does the AI agent do well it generates a actual working application, so it has generated a prototype for what we asked for directly in response to that user's prompt.

05:44 Now users can take a look at this prototype and click through it and maybe submit some feedback. So maybe they complain that the approval screen is a bit confusing and that purple background looks too much like AI slop. Well that feedback goes back to the AI agent. At which point the agent regenerates the prototype, the user clicks through again, and the agent keeps on generating and refining working code.

06:13 And speaking of working code, the next RAD phase is construction. Well, the agent is already doing that. It's generating a bunch of stuff like data schema, workflow logic, maybe wiring up some email notifications. And then for phase four, cut over. This is all about getting the thing into production. So the finance team demos the prototype and hey, turns out everybody loves it.

06:38 Then I suppose inevitably someone senior asks the obvious question. They say, hey, this works great. So when can the whole company have it? And in classic RAD, we know the answer. The prototype is the keeper and it eventually becomes the product. Well, that time has arrived. So Cutover should just be a matter of deploying this prototype into production, right?

07:05 Well, we've got to ask the question here, is it really a good idea to do that when AI has written all of the code? Let's take a look at that next. So should the finance team just deploy the expense approval prototype? Well, maybe not so fast. If we take a closer look at what the agent built, it might turn out that nobody's prompt ever said that, let's say, an employee can't their own expenses.

07:32 So it's more than possible that the agent never wrote a rule requiring second sign-off, which would mean I could file a $900 expense and then approve it myself. Delightful. And this kind of thing is actually pretty common. Studies of AI-generated code have found something like 45% of samples carry some type of security issue or security weakness in them.

08:00 So... So what's the fix to this? Well, those code generators of RAD's era had no chat window. An analyst fed the generator a spec of really what they wanted. Maybe the data model, the business rules. And then the code generation tool would take that spec and it would then generate some basic code out of that. So James Martin's plan always really had two halves.

08:30 It had a precise description of what it is that we actually want to do, what the system needs to build, and that description also was turned into actual working code as well so we could build the prototype. Well, think about how this works today. AI agents have given us a very good code building machine, that's for sure. And the description half, well that's coming from something else now and that's something else is called spec driven development.

09:10 So the idea is that discoveries from the prototype get written down as a spec. So the business rules, the acceptance criteria, the security requirements, like nobody ever their own expenses. Users clicking through the prototype... Well, they might never catch the self-approval loophole because they can't click on a rule that isn't there, but because it's in a spec, somebody from security reading that spec is gonna ask, hold on, what stops me from approving my own expenses?

09:41 And once that rule is in the spec, it becomes a test. The generated code now has to pass. So yes, the prototype is still a keeper. But what actually goes into production is everything the prototype taught us which is captured in that spec. Which means RAD's four phases have held up pretty well for a 35 year old methodology. Plan lightly, prototype early, build in short cycles and don't move over into production until the thing has been verified.

10:18 And as for James Martin and that 1982 book title, Application Development Without Programmers, 44 years on, I'd say that particular request is still waiting on its second sign off because the programmers, they are still very much here. They've just moved in the direction of writing the spec and verifying the result.