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This is a hard difficulty System Design question, commonly asked during Technical Screen rounds at OpenAI.

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This question is commonly asked for Software Engineer candidates at OpenAI during technical interviews.

Design a minimal ChatGPT with presets | OpenAI Interview Question

Quick Overview

This question evaluates a candidate's competency in designing multi-tenant conversational AI services with reusable, versioned presets, covering API design, data modeling, session state and context-window management, scalability, multi-tenant isolation, privacy, and observability.

System Design: Minimal ChatGPT-like Service With Reusable Presets

Context

Design a multi-tenant conversational AI service that supports reusable presets (system prompts) at user and team scope. A preset can be versioned, shared, approved, applied to a chat session, and soft-deleted. The service should expose public APIs, manage session state within context windows, and operate reliably and cost-effectively at scale.

Requirements

Specify the following:

Core components and their responsibilities.
Data model for presets:
- Fields and metadata.
- Ownership and scope (user, team, org, public).
- Versioning and soft delete.
- How presets are referenced in a chat session.
Public APIs:
- CRUD for presets, list and search, apply-to-session, clone and share.
- Request and response examples.
- Auth and RBAC considerations.
Session state handling and context-window management:
- How messages and presets combine into prompts.
- Truncation and summarization strategy.
Storage choices for messages and presets, indexing, and caching.
Scalability plan:
- Stateless workers, queueing and backpressure, autoscaling.
- Latency and SLO targets.
- Cost controls.
Multi-tenant isolation, rate limiting, and quota.
Safety and privacy controls:
- PII redaction, preset approval flow.
- Audit logging and observability.
Failure modes and fallbacks:
- Missing preset, model timeout, partial outage.
Extensibility:
- A B testing different presets, preset libraries, shareable links.
Capacity estimates for 100k DAU.
Sequence of calls for:
- (a) Create a preset.
- (b) Send a message using a preset.

Quick Overview

Context

Requirements

Specify the following:

Core components and their responsibilities.

Data model for presets:

Fields and metadata.
Ownership and scope (user, team, org, public).
Versioning and soft delete.
How presets are referenced in a chat session.

Public APIs:

CRUD for presets, list and search, apply-to-session, clone and share.
Request and response examples.
Auth and RBAC considerations.

Session state handling and context-window management:

How messages and presets combine into prompts.
Truncation and summarization strategy.

Storage choices for messages and presets, indexing, and caching.

Scalability plan:

Stateless workers, queueing and backpressure, autoscaling.
Latency and SLO targets.
Cost controls.

Multi-tenant isolation, rate limiting, and quota.

Safety and privacy controls:

PII redaction, preset approval flow.
Audit logging and observability.

Failure modes and fallbacks:

Missing preset, model timeout, partial outage.

Extensibility:

A B testing different presets, preset libraries, shareable links.

Capacity estimates for 100k DAU.

Sequence of calls for:

(a) Create a preset.
(b) Send a message using a preset.

Design a minimal ChatGPT with presets

Quick Overview

System Design: Minimal ChatGPT-like Service With Reusable Presets

Context

Requirements

Solution

Comments (0)

Design a minimal ChatGPT with presets

Quick Overview

System Design: Minimal ChatGPT-like Service With Reusable Presets

Context

Requirements

Solution

Comments (0)