The New Way to Build Software: Explain It.
The Digital creation process has been blocked by a complicated system of semicolons, brackets and logic that only the chosen ones can read. You needed to take years to learn how to talk to machines in order to build software. You needed to be a developer, engineer, coder. The New Way to Build Software: Describe It and Get It.
But what should happen should the basic assumption start to turn in the opposite direction? Suppose you learned the language of the machine, but, at last, the machine learned to understand you?
Here is the start of a new paradigm. The description is the new method to develop software. It is not some off-the-wall fantasy of a science fiction novel; it is what is happening in labs and startups worldwide today, driven by the technology of Generative AI and Large Language Models (LLMs). In this article, the author will discuss how this transformation is breaking down the walls, increasing the pace of innovation, and fundamentally transforming who qualifies to be called a builder.“Want to dominate with AI? Dive into our latest reviews of cutting-edge marketing tools at AI Smartly.
Punch Cards to Prompts A Brief History of How We Build

In order to appreciate the revolution, we need to know the evolution first. Software development has been a long struggle towards an increased level of abstraction.
Machine code. To write binary instructions (1s and 0s) which the hardware can execute directly, programmers actually needed to flip switches or punch cards. It was then tiresome, inaccurate and demanded a thorough understanding of the physical machine.
Term 2: The Rise of Compiler Languages: Fortran, C and C++. Such languages brought a titanic leap: abstraction. Developers were now able to write commands using a syntax that was similar to human language (such as if and for) which a compiler would then convert to machine code. The machine was acculturation to us.
Scripting Languages and Object-oriented programming languages: Java, Python, JavaScript. Complexity was further abstracted in this period. Developers started to think in terms of reusable objects and were able to create powerful applications using simpler and more intuitive scripts. Ready-to-use structures like React, Django or Ruby on Rails were also provided within web frameworks and were therefore much faster to develop.
The Low-Code/No-Code Revolution. Platforms, including Bubble and Adalo, were launched as visual programming. App development was open to non-programmers, allowing users to drag and drop elements of the UI and create logic blocks through which to define workflows. But these platforms were usually not very flexible and customizable.
Each of these steps brought us nearer to human thought and further to the hardware. The next abstraction, the last one, is to get rid of the code layer altogether. That’s where we are today.
What is the Mechanism behind Describe to Build? The Magic Behind the Curtain

The idea itself seems magic: you enter a sentence and an app is displayed. But the technology behind it is complex and interesting. It is mostly based on two major AI capabilities:
- 1. High Natural Language Processing (NLP): Current LLCMs such as GPT-4 of OpenAI are trained on a large part of the internet, that is, large libraries of publicly accessible code such as on GitHub. They do not only know the language, but they know the intent, context, and undertones of the concepts of development. When you write, “Create a form and add user emails to a Google sheet, the model breaks down to individual pieces of action: a user-facing form, a server API endpoint, and an automation using the Google Sheets API.
- 2. Code Synthesis Generative AI: The magic actually occurs here. After understanding the intent, the AI does not copy code, it creates a new useful code based on your request. It chooses the right programming language (e.g. Python on the back-end, HTML/JS on the front-end), organizes the files in the correct way, and also writes the required configuration files. Early versions of this include GitHub Copilot and Ghostwriter, a tool at Replit that serves as a pseudo-pair programmer and recommends lines of code. The following step is systems that create the entire application architecture with only one prompt.
Experiments such as GPT Engineer and Smol Developer are open-source experiments at this edge. But a new category of integrated platforms is now beginning to emerge in order to make this power available to all. GenesisAI is the leader of this pack; it is an application that is developed based on one strong principle: Describe your tool. We build it. It is the next step in this technology, beyond the experimental code generation, to a refined, end-to-end experience that provides a deployed program based on a natural language prompt.
GenesisAI in Action The New Paradigm Case Study.

To imagine how a platform like GenesisAI in operation can look like, we can use a hypothetical example. Suppose that a small business owner requires a tailor-made internal application to monitor the vacation requests of their employees.
The Old Way: they would have to employ a developer, create a specification document, undergo a development cycle, and spend thousands of dollars and several weeks.
The GenesisAI Way: The owner simply enters a prompt into GenesisAI: Build a password-protected employee portal in which employees can make vacation requests with dates and a reason. All requests should be displayed to managers in the form of a dashboard, and they should be able to approve or deny them. Then save all that in a database and mail a confirmation mail when approved.
GenesisAI would then: Be familiar with the prompt and break it down into user roles (employee, manager), feature (form, dashboard, auth) and integration (database, email).
Create the entire stack: a React front-end, a Node.js back-end with API endpoints and a SQL database schema.
Install the whole application to a live URL, and give the business owner a link to a fully operational, secure tool in minutes.
This is not a far off future technology, but rather the immediate implementation of the describe it philosophy that GenesisAI is introducing today.
Use Cases: Can You Build by Describing?

The potential is enormous but it is nice to be aware of the current abilities. This technology is good at developing certain kinds of applications in a short time.
Rapid prototyping and MVPs: It takes hours and not months to take an idea and turn it into a prototype that one can actually click on. This enables an amazingly rapid validation and feedback cycle with no upfront engineering cost.
Specialized Internal Tools: This is a killer application to this technology. Require getting a tailor-made dashboard to monitor team KPIs? csv clean up and formatting tool? An internal HR request portal? You can describe the tool and have a working one nearly instantly instead of dropping a ticket and waiting until the dev team has room on the bandwidth to allow the ticket to be processed. Firms such as Retool are already leaning into AI to support this functionality, yet some AI-native constructors such as GenesisAI are going further.
Robotization of Routine Work: Digital work of knowledge workers is unique and repetitive in many cases. You no longer have to manually copy data in and out of applications or reformat existing documents every week, you can simply explain how you work to an AI building tool and receive a tailor-made script or micro-application that will automate the process to perfection.
Learning and Exploration: To novice developers, explaining a concept and the way it is applied in the AI is an effective way to learn. It is just having a senior developer on-call 24/7 who can demonstrate to you alternatives on how to solve a problem.
These Are the Inevitable Questions: Limitations and The Human Touch.

Limitations and concerns are important to address with any transformative technology. The describe it model is an effective one but it is not a silver bullet.
Complexity Ceiling: This is useful in well-defined and specific uses, but it is generally impossible to describe a massively-complex system such as a new operating system or a social network that covers the entire world. The architecture demands some degree of subtle decision making that is not yet automated.
The Black Box Problem: The AI produces the code, where does the debugging, security, and optimization begin? Someone still has to develop the code (or someone very adept with it) to audit the code generated, verify that it is efficient, and verify it is secure. It is still important to understand the code it writes.
The I Don’t Know What to Ask For Problem: This is a paradigm that demands you to possess a vision. Unless you can tell us what you want, the AI will not construct it. Words of ambiguity will produce ambiguous outcomes.
This does not mean the end of the developers. Rather, it increases their position. The developers will stop writing boilerplate code and start being architects, auditors and innovators. Their usefulness will be more on the side of implementation, strategy, and complex systems management that these AI tools take up. Human touch can be more important than it has ever been in creative problem-solving and ethical supervision.
The Future is Talkative and Recurring

The model of describe it is only the tip of the iceberg. Discussion via conversation is the next stage of building.
User: “Build me a weather app.”
AI: Produces a simple application that displays the local weather.
User: “Add 5 day forecast and make the background indicate whether we are in sunny or rainy weather.
AI: Immediately pushes the codebase with the new features to the codebase.
This is a repetitive interactive cycle that makes the development process a genuine collaboration of human instinct and machine software. This future is already being demonstrated by platforms such as v0 by Vercel, and platforms such as GenesisAI can and will naturally integrate it.
In conclusion, there is the democratization of Development.
The capability to create software using a description of it is not only technically new; it is democratizing. It demolishes the last wall to digital creation, allowing marketers, entrepreneurs, scientists, and artists to create the tools they imagine without a technical co-founder.
The first ones are platforms such as GenesisAI, which transform this groundbreaking idea into an operational instrument. It does not take the place of substantial technical expertise but instead spreads the authority to create further. We are shifting to an era when only programmers can create software to the era when any person with a problem and a clear idea can create a solution. Creation has lost its language: Python or JavaScript is replaced by human will. And that is the deepest upgrade to the developer stack we have ever witnessed.
Email: adil.taskthegroup@gmail.com