Design an OpenAI chatbot in two hours

Q: Design an OpenAI chatbot in two hours

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.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Question

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

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

Design an OpenAI chatbot in two hours

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

Goal

Requirements

Solution

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Design an OpenAI chatbot in two hours

Overview

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

Goal

Requirements

Solution

Comments (0)