OpenAI API & ChatGPT Interview Questions Interview Guide

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I built a customer support chatbot using GPT-4, optimizing for accuracy over speed. I implemented guardrails to prevent misuse, logged conversations for feedback loops, and achieved 85% customer satisfaction.

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I analyzed usage patterns, implemented response caching for common queries, routed simple requests to GPT-3.5-turbo, reserved GPT-4 for complex problems. Reduced costs by 65% without quality loss.

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For domain-specific classification, fine-tuning beat prompting by 12% accuracy. The task had subtle patterns that examples couldn't capture. Fine-tuning cost $1200 but saved engineering time and improved reliability.

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Classify task complexity using heuristics or lightweight model, route simple queries to 3.5-turbo, complex to GPT-4. Monitor latency and quality, adjust thresholds weekly. Cache GPT-4 results aggressively.

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Function calling lets models request specific actions, enabling multi-step workflows. Use for: data retrieval, calculations, database operations. Chain multiple function calls for complex tasks.

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Stream tokens to client as they arrive, render progressively, handle connection drops with retry. Store all streamed content server-side for logging and recovery.

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Curate high-quality examples (500+ minimum), format consistently, validate outputs manually, split 80/20 train/val, monitor validation loss for overfitting, iterate based on results.