What does a complete system design answer include?

Requirements clarification, high-level architecture, API and data model, scaling strategy for the stated load, bottleneck analysis, and explicit trade-offs. The generated prompt asks for each of these.

Why specify CAP priorities?

Under network partitions you must trade consistency for availability or vice versa. Telling the model which to favor shapes the data store and replication choices it recommends.

Is this for interviews or real design?

Both. The same structure that earns marks in a system design interview also produces a sound first-pass architecture for real work — clarify, design, scale, analyze trade-offs.

How precise should scale numbers be?

Order of magnitude is enough. "10M daily users, 50k QPS peak, 5TB/year" lets the model size caches, shards, and queues appropriately; vague scale leads to vague designs.

Does it design the system itself?

It builds the prompt; your LLM produces the design. Treat the output as a strong starting draft to critique, not a final architecture.

System Design Prompt Builder

System design prompt builder

A good system design answer is not a diagram — it is a chain of reasoning: clarify the requirements, propose an architecture, size it for the stated scale, find the bottlenecks, and name the trade-offs you accepted. LLMs can produce that chain well, but only when the prompt forces the structure and supplies real numbers. This builder turns your system, scale, and priorities into a prompt that demands a complete, scale-aware design with explicit trade-offs — useful for interview practice and for real first-pass architecture alike.

How it works

You describe the system, enter scale requirements (users, QPS, data volume), choose your CAP-theorem priority, and list constraints or components to focus on. The tool assembles a prompt that walks the model through the canonical phases: restate and clarify requirements, sketch a high-level architecture, define the API and data model, present a scaling strategy that meets the stated load, analyze bottlenecks, and lay out the key trade-offs given your CAP choice. You pick the output format (prose, sectioned, or with a textual diagram). Everything runs locally until you paste it into your LLM.

Tips and examples

Give real numbers. “100M users, 80k QPS peak, 10TB/year” lets the model size shards and caches; without them the design stays generic.
Pick a CAP side. A payments ledger favors consistency; a social feed favors availability. Stating it changes the recommended data store.
Focus the components. If you only care about the write path or the caching layer, list it — the prompt will go deep there instead of spreading thin.
Ask for trade-offs explicitly. The best designs name what they gave up; the prompt requires this so you do not get an over-confident single answer.