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Design an OpenAI chatbot in two hours

Last updated: Apr 21, 2026

Quick Overview

This question evaluates a candidate's competency in ML system design and backend engineering for conversational AI, including architecture, session state and context management, streaming responses, resilience (rate limiting and error handling), observability, lightweight persistence, and safety when integrating a large language model API.

  • medium
  • EliseAI
  • ML System Design
  • Software Engineer

Design an OpenAI chatbot in two hours

Company: EliseAI

Role: Software Engineer

Category: ML System Design

Difficulty: medium

Interview Round: Technical Screen

Design and implement a minimal yet functional conversational chatbot using the OpenAI API within a two-hour coding exercise. Specify the architecture (API server, message schema, session state, context window strategy), how you will stream responses, and how you will handle rate limits, retries, and errors. Describe logging/metrics for quick debugging, a lightweight persistence option, and basic safety checks. Outline your project structure, key endpoints, and exact steps to run locally; you may optionally leverage an AI IDE like Cursor—explain how it speeds up delivery.

Quick Answer: This question evaluates a candidate's competency in ML system design and backend engineering for conversational AI, including architecture, session state and context management, streaming responses, resilience (rate limiting and error handling), observability, lightweight persistence, and safety when integrating a large language model API.

EliseAI logo
EliseAI
Sep 6, 2025, 12:00 AM
Software Engineer
Technical Screen
ML System Design
14
0

System Design Task: Minimal Conversational Chatbot (2-hour build)

Goal

Design and implement a minimal yet functional conversational chatbot using the OpenAI API that you can run locally within a two-hour coding exercise.

Requirements

  1. Architecture
    • API server
    • Message schema
    • Session state
    • Context window strategy (how you fit conversation into model limits)
  2. Streaming responses to the client
  3. Resilience
    • Handle rate limits (429), retries, and generic errors
  4. Operations
    • Logging and metrics for quick debugging
    • Lightweight persistence option
    • Basic safety checks (content moderation / guardrails)
  5. Deliverables
    • Project structure
    • Key API endpoints
    • Exact steps to run locally
    • Optional: How an AI IDE (e.g., Cursor) speeds delivery

Solution

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