When we started building interview preparation at Educative, the goal was never to predict every question a developer might encounter. I was more interested in making difficult interviews feel less arbitrary. System Design was a perfect example: strong engineers could understand databases, caches, queues, and distributed systems individually, yet still find it difficult to turn those pieces into a coherent architecture when somebody handed them an ambiguous problem.
That challenge became an important part of Educative’s Grokking journey. Over time, though, the interview surface around it grew much larger. The fundamentals remained useful, but the jobs developers were preparing for, and the systems those jobs involved, kept expanding.
System Design was a natural place to start
System Design interviews compress a surprising amount of engineering into a short conversation. You have to clarify requirements, reason about scale, decide where data belongs, think about reliability, make trade-offs, and communicate enough of that reasoning for another engineer to follow it.
That made System Design well suited to the Grokking approach. The value was not simply learning that a cache reduces repeated work or that replication improves availability. It was developing a structure for approaching a problem that initially had very little structure.
That remains central to how we teach System Design today. Educative’s currentGrokking Modern System Design Interview still spends substantial time on databases, caches, load balancers, queues, replication, consistency, sharding, capacity, and failure handling. It also uses a structured 45-minute interview approach and applies those ideas across complete design problems rather than treating the components as isolated definitions.
The interesting part of our journey is what had to grow around that foundation.
The interviews kept moving
Engineers still prepare for Meta, Amazon, Google, and Microsoft. But the set of technically demanding destinations now also includes AI companies, data platforms, fintechs, cloud providers, developer-infrastructure companies, and many other specialized engineering organizations.
That matters because different businesses put pressure on different parts of your System Design knowledge. A payments architecture can make idempotency, correctness, consistency, and auditability central. A data platform can push the conversation toward ingestion, partitioning, stream processing, storage, and query performance.
AI infrastructure introduces another combination. Inference can involve expensive compute, request routing, batching, accelerator utilization, latency, throughput, and capacity decisions. Anthropic, for example, has publicly described serving Claude across AWS Trainium, NVIDIA GPUs, and Google TPUs, with different optimizations required across those platforms. That is not evidence of a particular Anthropic interview question. It is evidence of how much broader the engineering environment around distributed systems has become.
The fundamentals did not disappear. They started showing up in more kinds of systems.
That is what “Modern” means to us
Grokking Modern System Design Interview is our attempt to prepare engineers for that broader surface without turning System Design into a collection of fashionable technologies.
Caching still matters. Partitioning still matters. Queues, replication, consistency, capacity planning, load balancing, and failure recovery still matter. What changes is the context in which an engineer may need to use them.
The current Educative curriculum combines those foundations with systems such as YouTube, WhatsApp, Uber, and Google Maps, while extending the discussion into ChatGPT and AI/ML infrastructure. It also emphasizes reasoning through ambiguous requirements, identifying bottlenecks, defending trade-offs, and communicating a design under interview constraints.
That distinction matters to me. Adding an AI label to an architecture does not make it sophisticated. A newer workload is useful educationally when it forces you to apply familiar engineering ideas under different constraints.
Grokking grew beyond System Design
Eventually, we started encountering the same problem elsewhere: the interview itself was becoming broader.
A software engineer may still need coding and System Design, while an ML engineer may be evaluated on model selection, data pipelines, experimentation, metrics, and ML System Design. Other roles can add architecture, API design, object-oriented design, engineering-management evaluation, or behavioral rounds.
So the Grokking ecosystem expanded with that surface. Educative now has Grokking preparation for coding interviews, Machine Learning interviews, ML System Design, and behavioral interviews alongside Modern System Design. Our broader interview-preparation catalog also covers areas such as API Design, OOD principles, Machine Learning, and Engineering Manager preparation.
The common idea is more important than the number of categories. A good interview-prep resource should take something that feels unpredictable and expose enough recurring structure that an engineer can reason through it.
“Interview prep for all” means looking beyond five companies
For a long time, technical interview preparation was almost synonymous with FAANG preparation. Those companies still matter, and the engineering ideas associated with them remain enormously useful.
But developers do not all want the same five jobs.
Someone may be interviewing at an AI lab, fintech, data platform, developer-tools company, cloud provider, startup, or large enterprise. Even within one company, an infrastructure engineer and a product engineer can face very different technical expectations.
That changes what I think a useful interview-prep ecosystem should optimize for. Company-specific preparation has value, particularly near the end of the process, but the deeper goal should be transferable engineering ability. You want to recognize the shape of a problem even when you have never seen that exact problem before.
That principle connects coding patterns, Modern System Design, Machine Learning, and the other areas we have built around Grokking. The problem changes, but the educational challenge is remarkably consistent: how do we help developers move from knowing material to being able to use it when the interviewer removes the safety net?
A journey that should keep expanding
When I look at Educative’s Grokking journey, the part I find most interesting is not that System Design became an important interview-prep category. It is how the same educational idea kept finding new places to apply.
We began by helping developers make sense of difficult technical interviews. System Design became a major part of that work because it turned ambiguity into something engineers could systematically practice. Grokking Modern System Design Interview expands that preparation across the wider architectures and constraints engineers encounter now, while the broader Grokking ecosystem carries the same philosophy into coding, Machine Learning, ML System Design, behavioral preparation, and other parts of the interview loop.
For me, that is the thread connecting all of it: take difficult engineering judgment and make it learnable.
The questions will keep changing. The useful part is understanding deeply enough that you can change with them.
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