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System Design Diagrams with ChatGPT

By 5 Minutes or Less     Updated Feb 28, 2024

Dive into the revolutionary impact of AI on system architecture through the lens of a groundbreaking experiment using ChatGPT to design a web application architecture diagram.

1. The Experiment with ChatGPT

It started with a simple request to ChatGPT: design a succinct architecture for a web application centered on analytics, with a React.js frontend and a microservices-based backend using PostgreSQL. To my surprise, the guidance it provided was spot-on.

For the frontend, it proposed using REST APIs or GraphQL along with state management via Redux, not forgetting data visualization needs with D3 and Chart.js. It also suggested architectural best practices, like isolating database schemas per microservice.

However, the proposal to use MQTT struck an off chord since it neglected the cardinal rule that a database should only be accessed through backend microservices. But I was soon to find out that ChatGPT had even more insights to offer.

2. ChatGPT's Architectural Decisions

ChatGPT displayed its tech-savvy nature by recommending opinionated choices such as implementing an API Gateway and using Docker containers. This was notable because it did not blindly follow my cues; it provided alternatives and justifications for its suggestions.

Upon refining the requirements, indicating a duo of backend microservices with specific functionalities, it reflected on the enhanced need for speed and scalability, promptly nominating Redis as a fitting caching solution.

This led to a curious back-and-forth about the combination of a load balancer and an API Gateway. The dialogue with ChatGPT revealed a nuanced understanding of trade-offs, pushing the boundaries of AI's role in complex system design.

3. Translating to Cloud Solutions

With a nudge towards the cloud, the experiment entered a new phase. ChatGPT confidently transformed the abstract components into their AWS counterparts, exhibiting a grasp of cloud-specific products.

The diagram morphed once more with some tweaks here and there, like removing a load balancer in favor of a database replica. This showed how the AI could aid in drafting an adaptable high-level architecture, ready for real-world application.

The cherry on top was ChatGPT listing the pros and cons of this architectural setup – a comprehensive analysis someone might well stumble upon in an official document. This insight suggests how AI could become a formidable ally in the architect's toolkit.

4. Towards Autonomy in Architecture

Venturing further into the realm of possibility, I considered the potential of AI that not only designs but also autonomously evolves architectures to better meet operational demands and cost efficiency.

Imagining a future where AI could shift a microservice from a container to a serverless function on the fly to save costs is not just a pipe dream. This scenario underscores the burgeoning reality of auto-adaptive architectures.

We stand on the brink of a new dawn where AI's invasive roots could extend deep into the domain of system design, requiring us to ponder the ethical and practical aspects of relinquishing control to these intelligent entities.

5. Community Reactions

One user was curious about the practical steps, querying on how to execute the script provided by ChatGPT – a testament to the practical value found in the experiment's findings.

Others expressed excitement over the potential educational content, suggesting a video on Data Science with ready-to-use code snippets – possibly a niche where AI could further supplement human learning.

A more cautionary perspective mused on how corporate interests could manipulate AI's impartiality, drawing parallels with Google Maps' routing controversies and pondering the potential effects on AI's neutrality in solution architecture.

6. Concerns and Considerations

Some members of the technological community argue that the inherent vagueness in client requirements will always necessitate a human touch, casting doubt on the complete replacement of developers by AI like ChatGPT.

The atmosphere of concern is not without merit, with some community members voicing worries about technological dependence and the ethical implications – a conversation that is as pivotal as the innovation itself.


In an era where AI is reshaping development landscapes, ChatGPT demonstrates an intriguing proficiency in system design. By requesting ChatGPT to sketch out an architecture for a web analytics dashboard, not only did it convey a detailed structure complete with technology stacks, it also generated a system diagram through code. This experiment sheds light on how AI could amplify the productivity of solution architects and perhaps foreshadows an even more autonomous future in software design and maintenance.