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Design an AI Code Assistant

Build a GitHub Copilot competitor that provides intelligent code completion, explanations, and debugging assistance using large language models.

GenAI SystemsReal-time MLCode Intelligence
Q: What's the expected scale in terms of developers and requests?
A: We need to support 10M+ developers globally with 100M+ requests per day. Peak load during US/EU business hours hits 50K requests per second.
Engineering Implications: This massive scale requires distributed architecture with multiple data centers, sophisticated caching strategies, and auto-scaling inference infrastructure. We'll need CDN distribution and edge computing for global latency optimization.
Q: What types of AI assistance should we provide?
A: Core features include real-time code completion (<100ms), code explanation, bug detection, refactoring suggestions, test generation, and documentation assistance.
Engineering Implications: Different features have different latency/accuracy trade-offs. Completion needs ultra-low latency with good-enough accuracy, while explanation can tolerate higher latency for better quality. We'll need specialized models for each task.
Q: What programming languages and IDEs need support?
A: Must support 50+ major languages (Python, JavaScript, Java, C++, Go, Rust, TypeScript, etc.) across all major IDEs (VS Code, JetBrains, Vim, Emacs) and cloud environments.
Engineering Implications: Language diversity requires multilingual models with language-specific fine-tuning. IDE integration needs standardized plugin architecture with real-time synchronization protocols.
Q: What are the privacy and security requirements?
A: Enterprise customers require on-premises deployment, code never leaves their environment. SaaS version needs encrypted transmission, no persistent code storage, GDPR compliance, and SOC 2 certification.
Engineering Implications: Privacy-first architecture with differential privacy for telemetry, local processing options, and zero-knowledge proof techniques for model improvement without exposing code.
Q: What's the business model and pricing structure?
A: Freemium model: 100 completions/day free tier, $10/month professional (unlimited), $25/month enterprise (on-premises option). Revenue target: $500M ARR within 3 years.
Engineering Implications: Pricing drives infrastructure cost optimization. Free tier needs aggressive caching and model compression. Enterprise pricing justifies dedicated inference infrastructure and premium model variants.
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Interview Practice Questions

Practice these open-ended questions to prepare for system design interviews. Think through each scenario and discuss trade-offs.

1

Enterprise Scale: Design a code assistant for 100K+ enterprise developers across different programming languages. How do you handle multi-tenancy, code privacy, and varying usage patterns while maintaining <100ms response times?

2

Context Optimization: A developer is working on a large codebase (10M+ lines). How do you intelligently select the most relevant context for code suggestions while staying within the model's token limits?

3

Model Serving: Your inference costs are growing rapidly as usage scales to 50K QPS. Design a cost optimization strategy including model quantization, caching, and request batching while maintaining quality.

4

Continuous Learning: How would you build a feedback system that continuously improves code suggestion quality? Include user feedback collection, model retraining pipelines, and A/B testing framework.

5

Security & Compliance: Design security measures to prevent code injection attacks, ensure PII detection in code, and meet enterprise compliance requirements (SOC2, GDPR) for a global code assistant service.

6

Offline Capability: Some enterprise customers need offline code assistance for sensitive environments. How would you design a hybrid online/offline system that maintains quality while working in air-gapped networks?