- Co-authored by Matt Paige · Omar Shanti · Luis Villalobos · Randall Ramirez · Josue Alpizar · Melissa Malec
Right now, every engineering leader is feeling it. The sense that other teams are already making AI work, while you’re still stuck in scattered experiments and half-adopted tools.
You’ve probably tried a few things. Maybe your devs are poking around in Cursor, or pasting prompts into ChatGPT between sprints.
But what you don’t have is a repeatable, team-wide system and applicable methodology for building with AI. Not just as a tool, but as a true part of your workflow.
Until today.
Today, we’re sharing our methodology for building with AI. It’s been formed off the back of using GenAI to help our teams do more with less across dozens of clients.
It’s called Generative-Driven Development (GenDD) and it’s our way of codifying the efficiency gains of AI across product, discovery, design, development, QA, DevOps…every part of the software development lifecycle.
In this piece, we’ll show you what GenDD is, why it works, and how your team can adopt it to build better and faster.
What is GenDD (Generative-Driven Development)?
Generative-Driven Development, or GenDD for short, is our approach to building software with AI. It aims to embed AI, agents, and agentic workflows into the entire software development life cycle.
In practice, that means AI handles execution-heavy work like code generation, refactoring, documentation, even interface design. All the while, us humans stay focused on the bigger picture as orchestrators—defining goals, validating direction, solving problems that actually matter.
It’s a better way to build.
Why are we sharing it with you? Because Hatchworks AI exists to empower technologists to solve real-world problems in the best ways. And so far, this methodology is the best way to use AI across the software development lifecycle.
Why We’re Doubling Down on GenDD (and Why We Created It In the First Place)
The question of how human intelligence and artificial intelligence can best work together has been on our minds ever since ChatGPT took us all by storm in late 2022.
We’re now at a pivotal moment—what we call AI’s inflection point—where three forces are converging to reshape how software gets built
- The Commoditization of AI Models: Models have become more powerful and more affordable. Open-source efforts have accelerated access, making AI available to a broader range of teams than ever before.
- The Emergence of Reasoning Capabilities: New architectures like Mixture of Experts (MoE) and Chain-of-Thought are giving AI the ability to reason through complex problems—tasks that used to belong exclusively to humans.
- The Rise of AI Agents: Agents come equipped with orchestration layers and tool access, allowing them to observe, act, and adapt autonomously. Now that the use of AI agents are growing, software systems can be more dynamic and responsive.
All three forces are converging to rewrite the rules of software development.
GenDD is our response. It’s how we’re not just keeping up with change, but leading it—rebuilding the development process around AI to make it faster, more intelligent, and more human where it matters most.
It’s also the product of real-world engineering pains—projects that took too long, standards that didn’t scale, and tools that promised more than they delivered.
We built GenDD because the way we were working stopped making sense, especially in a world with AI.
Who is GenDD For?
If you’re shipping real products, coordinating across roles, and feeling the pressure to deliver faster without burning out, then GenDD was built for you.
It’s for teams trying to answer questions like:
How do we accelerate our velocity without throwing
How do we get our people on board—not just the
While anyone can pick up and apply the methodology, we’re writing this specifically for the engineering teams in the thick of it. The ones trying to move fast and get it right.
We created it for ourselves as much as for our clients. In fact, we have a whole GenDD manifesto we follow that keeps our developers focused on the method that works. Our next section gives a glimpse into the core principles from our manifesto.
Want to be trained on all things GenDD?
Sign up for our GenDD training workshop. We’ll teach your team how to adopt the methodology, integrate AI, agents, and agentic workflows into your process, and start building the GenDD way.
By the end, you’ll have a repeatable system for building with AI—one that’s designed for real teams, real products, and real impact.
Fill out this form, and we’ll take it from there.
The Core Principles of GenDD to Keep Top of Mind ⬆️ 🧠
Think of these principles as your mental models for working the GenDD way. Rather than rules, they’re guardrails.
Follow them, and you’ll stay on track as you integrate AI into your development process.
1. Context is King
AI can do a lot, but it doesn’t know anything about your project until you tell it. You need to onboard it just like you would a new teammate who can work across your entire stack but has zero context until you provide it.
That means it’s on you to be clear. Supply foundational project info, share task-specific details, and give it a sense of where you are in the life cycle of your project, and where you’re going next.
PS. There are several cool approaches we have crafted as part of GenDD to make this both easier and more systematic.
Your job is to reduce ambiguity. The clearer your context, the better your results.
Say you’re working on a TypeScript monorepo and want help generating a new microservice. If your prompt is just “generate an API,” you’ll get something generic.
Instead, treat the LLM like a teammate you respect. Bring it into the fold. Add details like your auth method (JWT), preferred HTTP library (Axios), the surrounding services it’ll integrate with, and your folder structure. Share the user story, and even include your API contract so it can also write unit tests.
Then the AI can generate something aligned to your architecture from the start.
2. You’re the Orchestrator. AI is the Executor.
In GenDD, your role is to guide strategy, make decisions, and lead the direction of the work. The AI handles execution, but you’re still the one setting the vision and representing the voice of the end user. That requires a co-creation mindset.
Let’s say you’re building a frontend flow and using AI to scaffold components in React with Tailwind. The AI can generate clean components fast, but only if you’ve done your part.
That means thinking through the architecture: how will state be managed? Where should logic live? What edge cases matter?
You can even bring in another AI agent to help plan. Ask it to surface gotchas, propose a flow, or highlight integration points based on past projects. LLMs have seen thousands of builds so you might as well learn from them.
But it’s still on you to frame the problem, validate the plan, and configure the agent to help: what tools it has access to, what system prompt it runs on, and what constraints it needs to follow. That’s orchestration. That’s the job.
If you don’t set those boundaries, the AI might guess—or worse, get it wrong. But when you guide it with intentional structure and constraints, it can build usable components in seconds.
3. Build in Tight Feedback Loops
GenDD relies on human-in-the-loop feedback where humans actively review, refine, and steer AI at every step.
Let’s say you’re working with a legacy codebase.
Instead of pasting in an entire file and asking AI to “clean it up,” you begin with a scoped interaction.
First, have the AI explain what the module does. Then, prompt it to propose a new module structure and break it into clearer, more focused functions. From there, ask it to generate user stories and acceptance criteria for the new structure. Once reviewed, you can move into execution, validating each step as you go.
This kind of scoped, human-in-the-loop approach helps you maintain control and context, while still moving fast.
You can evolve your processes to include agents-in-the-loop feedback too. That’s where AI agents are purpose-built to act as checks and balances.
4. LLMs Make Mistakes (Just Like Humans)
AI will mess up. It just will.
It might hallucinate logic, misinterpret your intent, or write something that seems plausible but completely misses the mark.
Imagine that you ask GenAI to write a regex for email validation. It looks good at a glance but on closer inspection, it allows @.com or restricts uncommon but valid domains. It might struggle with names that contain hyphens or apostrophes. Or it might even reference a library method that doesn’t exist.
From naïve algorithms to outright hallucination, there is a whole range of ways that AI can get it wrong.
That’s why every GenDD workflow has a human-in-the-loop. You need to treat AI like a junior dev who works fast but needs a second set of eyes.
5. AI Is a Force Multiplier
AI isn’t constrained by skill gaps or headcount. It adapts instantly to any tech stack—front-end, back-end, any language or framework—letting you scale development on demand without hiring additional talent. Your capabilities within your existing team expand without increasing your overhead.
For example, you can guide it to write your Prisma schema, scaffold your REST endpoints, generate unit tests, and even help with Postman collections.
It lowers the barrier to entry while still requiring your judgment to validate, refine, and connect the dots. AI makes the work more accessible but doing it well still takes human direction.
6. With Great Power Comes Great Responsibility
AI can generate production-ready code at incredible speed but it can also introduce vulnerabilities, leak data, or violate compliance standards just as fast.
Many people are ‘vibe coding’ which is fun to experiment with but opens your environment up to blind spots. GenDD keeps the pace of production that vibe coding has but gives your team a methodology that prevents them from creating poorly thought-out solutions that don’t solve much of anything.
Think of GenDD as the antidote to vibe coding.
How GenDD is Reshaping Development Teams
Most software teams today still follow a structure designed for the pre-AI world. Teams are typically large, with specialized roles meant to cover every phase of the development lifecycle—from discovery to delivery.
But as teams grow, so does the complexity of coordination. Every new role adds a node of communication, increasing friction, creating more handoffs, and introducing more chances for misalignment.
Discovery might happen in one pod, delivery in another. Product managers hand off to designers, who hand off to engineers, who loop in QA. It’s not wrong. But it’s no longer optimized for what’s possible with AI.
GenDD reshapes that model:
The GenDD team structure centers on three core human roles—supported by AI at every stage of the product and engineering lifecycle.
Instead of building large, role-heavy teams around linear processes, GenDD centers on three core human roles: the Agentic Product Strategist, the Agentic Engineer, and the Agentic QA Engineer.
Each one answers a critical question:
The Agentic Product Strategist represents both the business and the user. They use AI to help guide discovery, define the vision, and keep the end goal in sight.
The Agentic Engineer brings that solution to life—working closely with AI, agents, and agentic workflows to design, build, and refine the system.
And finally, the Agentic QA Engineer ensures the result meets a high bar. From test generation to system validation, they leverage AI to enforce quality at every layer, not just after code is shipped.
Each role is an orchestrator in their own right, steering AI, defining intent, and refining outputs through tight feedback loops. They form a tightly aligned team that collaborates with each other amidst a network of specialized AI capabilities.
There are also optional supporting roles such as the Agentic Designer. It’s not required for every project but when you need stellar UX and UI for an external facing application, you’ll find them invaluable. They’ll use AI tools to accelerate their project while sense checking how best the app can meet usability needs.
Fewer roles means fewer communication nodes…and that’s intentional. With smaller, more focused teams, coordination becomes simpler.
And because the methodology is modular, the model scales easily. As a result, larger projects can be supported by adding more Agentic Product Managers and Engineers as needed.
A Software Development Snapshot–Before and After GenDD
But how does that acceleration happen? In the table below, we’ve broken down the core differences between traditional SDLC and GenDD:
| Category | Traditional SDLC | GenDD | Delta (Key Shift) |
|---|---|---|---|
| 🔍 Requirement |
1. Manual research 2. Manual persona mapping and user story writing 3. Manual interview design and synthesis 4. Facilitated workshops 5. Manual entry into SDLC tools |
1. Strategy alignment using AI-assisted research 2. Persona and user story generation using GenAI 3. AI-assisted interview design and synthesis 4. Workshops enhanced by tools like FigJam 5. Automated story upload via MCP 6. Auto-generated acceptance criteria and test cases |
Shift from manual, siloed input to AI-driven automation and integration across tools |
| 🎨 Design |
1. Lo-fi wireframes and clickable prototypes for validation 2. Hi-fi comps for engineering handoff |
1. Interactive low-code prototypes (Lovable, V0) 2. Reusable component libraries for rapid dev |
Shift from static assets to dynamic, low-code prototypes and reusable components |
| 💻 Dev |
1. Manual architecture and coding 2. Manual documentation |
1. AI-audited architecture patterns 2. Auto-generated technical documentation 3. Code generation from structured inputs |
Transition from hand-coded systems to AI-augmented development and documentation |
| 🧪 Testing | 1. Manual test case creation and execution |
1. AI-generated unit and integration tests 2. Scenario simulation using natural language inputs |
Testing moves from reactive and manual to proactive and automated |
| 🚀 Deploy | 1. Manual release via CI/CD pipelines | 1. Automated deployment via policy-driven workflows | Deployment evolves from scripted manual triggers to self-service or rules-based automation |
| 🔁 Review | 1. Manual sprint demos and stakeholder feedback sessions | 1. Continuous review cycles driven by natural language feedback | Feedback loops become continuous and conversational rather than periodic |
GenDD in Action: What It Looks Like In Practice
The traditional software delivery cycle? Linear, slow, and built for a world where humans handled everything by hand. With GenDD, the process becomes more collaborative, faster-moving, and designed for a human-in-the-loop approach.
Here’s a step-by-step look at how we used GenDD to go from design to deploy in way less time.
Scenario: Building a Healthcare Registration Form
We needed to build a new user registration flow for managing healthcare data. Here’s how we tackled it using Cursor and GenDD.
Step 1: Start with a Visual Input
We took a screenshot of the new registration form design and dropped it into Cursor.
<ExistingForm> component.”→ Cursor then generates a working React component, complete with layout, input fields, and styled classes.
Step 2: Review, Adjust, and Orchestrate
As orchestrators, we stepped in to evaluate the structure:
- Were all required fields included?
- Were they using the right input types and validation attributes?
- Did the styling match our design system?
A quick round of prompts helped fix inconsistencies and align it to our standards.
Step 3: Generate Backend Logic
With the frontend ready, we shifted to backend logic—still within Cursor.
→ Cursor scaffolds the endpoint, adds input validation, and even reuses existing email logic from our codebase.
Step 4: Tight Loops for QA + Improvement
From there, we ran tests, iterated on small changes, and cleaned up the flow. At every step, the human stayed in the loop, reviewing outputs, catching edge cases, and adjusting prompts as needed.
Throughout every stage, the human is the orchestrator that helps the AI execute tasks that would otherwise take up much more of our time. The result is a fully functional UI and backend flow, built and refined in minutes.
GenDD Tools for Your Tech Stack
One of the best parts about GenDD? It’s tool-agnostic. The methodology works across your stack, your team size, and your preferred tooling.
That said, certain tools can make getting started (and scaling) with GenDD a whole lot easier. Here’s a quick breakdown of options, depending on your team’s needs and technical depth:
Tier 1: Rapid Prototyping & Non-Developers
Perfect if you want to bring ideas to life fast, without writing any code. Great for prototyping.
- v0: Instantly turns natural language prompts into working UI components and app prototypes with minimal technical knowledge required.
- Loveable: An AI-powered builder that helps you turn ideas into prototypes and full apps fast, combining design and development into a seamless, no-code experience.
- Bolt: A no-code AI app builder that quickly transforms simple prompts into functional apps, designed for fast idea validation without technical complexity.
Tier 2: Full-Featured AI IDEs for Builders
For teams who want to get hands-on but still appreciate guidance from the AI.
- Replit: A powerful, browser-based IDE with AI-native coding support and instant deployment.
- Firebase Studio: Google’s AI-driven app builder that blends AI code generation with Firebase’s powerful backend services for an end-to-end experience.
Tier 3: Advanced AI Coding IDEs for Engineers
Best suited for engineering teams looking to go deep into AI-native development environments.
- Cursor: A powerful AI coding environment that feels like VS Code but deeply integrated with AI agents for proactive coding, debugging, and refactoring.
- Cline: An open-source AI-first coding environment that gives engineers fine-grained control over multi-agent coding workflows.
- Roo Code: A polished, enterprise-ready version of Cline designed for companies to deploy AI-native development at scale.
- Windsurf by Codeium: A powerful agentic IDE that can both collaborate with you like a Copilot and tackle complex tasks independently like an Agent.
- Aider (open source): A lightweight, terminal-based open-source AI pair programmer that edits your real codebase conversationally, ideal for highly technical users.
- Devin: Agentic AI software engineer designed to act as an additional engineer on your team.
- GitHub Copilot: The original AI paired programmer now with Agent mode.
Turn Theory Into Practice: Build the GenDD Way
The methodology is here. The tools available are growing in numbers. But if you want to go from theory to practice, we’ll help you get there faster.
At HatchWorks AI, we’ve been running GenDD in the real world—training teams, integrating AI agents into production workflows, and helping businesses like yours move faster without losing control.
Your team could be next. We’ll teach them how to:
- ✅ Make GenDD part of your daily development
- ✅ Integrate AI into your existing stack
- ✅ Build a repeatable, AI-native system for delivery
Fill out the form, and let’s get your team building the GenDD way.
