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AI Chat Text Area (ChatGPT-Style)

By FrontendAtlas Team · Updated Jan 31, 2026

Design a minimal ChatGPT-like experience in a text area: a user types a prompt and receives a streaming response. Cover the frontend UI, the client-side state model, and how the server stores conversation history (database choice and trade-offs). Key considerations: - Message list + composer…

Requirements exploration
Architecture / High-level design

What you’ll build / What this tests

This premium front-end system design system design focuses on AI Chat Text Area (ChatGPT-Style). You’ll apply real-time and streams thinking with intermediate level constraints. The prompt emphasizes Design a minimal ChatGPT-like experience in a text area: a user types a prompt and receives….

Learning goals

Decompose AI Chat Text Area (ChatGPT-Style) into front-end components, state, and data flow.
Apply real-time, streams, state-management 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 minimal ChatGPT-like experience in a text area: a user types a prompt and receives…
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: real-time, streams

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.