System Design is evolving – but most engineers aren't ready.

Here are 6 ways to level up your design skills for success in the AI era.

Apr 09, 2025

Generative AI is transforming everything from developer workflows to product roadmaps. But while everyone’s chasing the next breakthrough model, the real bottleneck is the system that serves it.

Even OpenAI can’t keep everything online when demand spikes (as we saw again last week, on March 31st, likely due to load from the Ghibli image generator).

In the GenAI era, success isn’t just about what a model can do. It comes down to smart System Design.

Generative AI System Design is the practice of designing systems that make AI useful, safe, and scalable in the real world.

Whether you're building AI-powered features, integrating APIs, or scaling infrastructure, understanding GenAI System Design is how you ensure your product doesn’t fall over the moment it gets real traffic — and gives you a competitive edge.

Today, I'll cover:

Traditional System Design vs. Generative AI System Design
Why GenAI System Design will give you an advantage
Your 6-step roadmap to level up

Let's go.

Traditional System Design vs. Generative AI System Design

Generative AI System Design is the discipline of designing distributed systems that reliably deliver AI-powered experiences at scale.

It builds on traditional System Design, where scalability, latency, and uptime still matter. But GenAI introduces quirks that shift how you think about every layer of the stack.

Let's break that down.

Traditional systems are built on predictable inputs and outputs, with known variables and clear contracts. You optimize for speed, scale, and uptime.

On the other hand, GenAI systems are:

Probabilistic
Unpredictable
Packed with moving parts you don’t fully control (e.g., dynamic model responses, state that spans across sessions and users)

With Generative AI, you're building infrastructure for systems that evolve, behave fuzzily, and can spike costs without warning. (If that sounds hard to design for, it's because it is).

Generative AI also adds system constraints you rarely see in traditional infra:

Vector-level caching (cache meaning, not just content)
Prompt engineering becomes part of your infra stack
Non-traditional failure modes like hallucinations, token cliffs, or degraded quality

But for all its quirks, Generative AI System Design is a growing discipline with best practices and principles you can use to navigate complex designs.

Why Generative AI System Design gives you an advantage

As AI gets integrated into nearly every product surface, companies increasingly need developers who know how to build systems that deliver AI services reliably, securely, and at scale.

While our models are getting pretty good, our systems are the bottleneck.

We’re watching this play out in real time, as companies race to launch Generative AI features and products and hit hurdles:

Amazon had to slow down its Alexa upgrade after hitting limits around latency, reliability, and hallucinations.¹
Apple had to pause its AI news summarization feature as it became inaccurate, leading to misinformation.²

The best GenAI products of tomorrow won’t be the ones with the “best” model. They’ll be the ones with the best System Design behind the model.

Generative AI System Design isn't just for those building LLMs.

Generative AI System Design is relevant if you're:

Integrating LLMs into existing product features like chat, summarization, or search
Building applications on top of models, whether through retrieval-augmented generation (RAG) or agent workflows
Scaling infrastructure to support growing usage, unpredictable demand, and cost constraints.
Improving model output quality through evaluation, feedback loops, prompt engineering, and system tuning

Knowing Generative AI System Design will open doors for you to lead new initiatives, step into Staff+ roles, or move into high-impact AI platform teams.

Let's talk about how you can learn this skill.

Your roadmap to Generative AI System Design

Here’s a roadmap to help you get fluent in Generative AI System Design.

1. Start with Distributed Systems basics

If you're new to infra, learn the fundamentals: Queues, caching, retries, fault tolerance, load balancing.

These are still the building blocks in Generative AI System Design, so give yourself a quick refresher if you're rusty.

2. Understand ML system fundamentals

This helps you debug, optimize, and reason about what's actually happening under the hood.

Learn about:

Transformers (the architecture behind LLMs)
Embeddings (how models represent meaning)
Inference pipelines (the steps to generate a response)

3. Get familiar with inference infrastructure

Understand the platform-level stuff that affects performance (even if you're using a hosted API):

GPUs and how they're used for model inference
Token quotas and how they limit context
Batching and concurrency to serve many users at once

These concepts help you spot (and fix) bottlenecks before they hit production.

4. Practice observability for GenAI debugging

GenAI systems don’t fail like traditional ones. They hallucinate, degrade quietly, or spike latency and cost without warning.

Start building your observability muscle by:

Logging prompts and outputs to see how small changes affect responses
Tracking latency across different inputs and model settings
Simulating failure modes like malformed inputs, giant prompts, or model unavailability

You can’t debug what you can’t see — and in GenAI, things will get weird. Observability is your early warning system.

5. Learn how to design for failure

GenAI systems will break. To stay resilient, we need:

Prompt fallbacks (when the first response doesn’t work)
Model tiering (fallback to smaller/cheaper models)
Circuit breakers (protect your infra from overload)

💡 Try sketching a fallback plan for one AI feature in your product (e.g., what happens if GPT-4 is down?).

6. Build something small (and break it on purpose)

Learning happens faster when you build. Pick a mini-project (like an AI Q&A bot or summarizer) and simulate failure modes like outages or slow responses.

These bite-sized projects will teach you how GenAI infra behaves in the wild (and how to tame it).

Designing for tomorrow

We always talk about designing systems that can scale for years to come. But with Generative AI becoming integral to modern apps, that future is arriving fast. Designing for tomorrow now means mastering the systems that serve AI today.

I hope you make a plan to learn Generative AI System Design — for yourself, and for the future of the tech industry.