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AI Image Generation MVP (Two-Week Design)

By FrontendAtlas Team · Updated Jan 31, 2026

Design a two-week MVP for integrating an image-generating AI into a mobile or web app. Focus on the frontend UX, the async backend workflow, and how you would ship a usable feature quickly with clear trade-offs. Key considerations: - Prompt input, generation states, and error…

Requirements exploration
Architecture / High-level design

What you’ll build / What this tests

This premium front-end system design system design focuses on AI Image Generation MVP (Two-Week Design). You’ll apply ux and api-calls thinking with intermediate level constraints. The prompt emphasizes Design a two-week MVP for integrating an image-generating AI into a mobile or web app. Focus….

Learning goals

Decompose AI Image Generation MVP (Two-Week Design) into front-end components, state, and data flow.
Apply ux, api-calls, real-time constraints to a real client architecture.
Define performance budgets and UX trade-offs for the interface.
Plan instrumentation and resilience for user-facing failures.

Key decisions to discuss

Choose a data model that matches UI reads and writes.
Pick a pagination + caching strategy to keep the UI fast.
Select a realtime transport and reconnection policy.
Set performance budgets (LCP/INP) and validate with telemetry.

Evaluation rubric

Architecture: clear client data flow and component boundaries.
Performance: budgets + caching/virtualization where needed.
Resilience: offline/error states and retries defined.
Observability: metrics/logs to validate UX and perf.

Constraints / Requirements

Design a two-week MVP for integrating an image-generating AI into a mobile or web app. Focus…
Requirements exploration
Architecture / High-level design
Data model
Interface definition (API)
Optimizations and deep dive
Assume real users and real latency; budget for perceived performance.
Define client-side caching and invalidation strategy.
Plan pagination or windowing for large UI lists.
Batch UI updates and handle reconnection/backoff.

Mini snippet (architecture sketch)

Constraints:
- P95 UI response < 200ms
- 1k+ events/sec during peak
- Focus: ux, api-calls

Components:
- Client cache + pagination
- Realtime updates (SSE/WebSocket)
- Observability + error states

Endpoints:
- GET /feed?cursor=
- POST /events

Common pitfalls

Ignoring pagination/virtualization for long feeds.
Underestimating client performance budgets.
Overfetching data or failing to cache responses.
Skipping failure states and retry UX.