OpenAI API Interview Questions 2026
OpenAI API interview questions covering GPT models, embeddings, function calling, fine-tuning, token optimization, and production integration patterns.
How do you implement function calling with the OpenAI API?
Function calling allows the model to output structured JSON to call your functions. Define tools: [{type: "function", function: {name: "get_weather", description: "...", parameters: {type: "object", properties: {...}}}}]. Send to API with tools parameter. If response.choices[0].message.tool_calls exists, execute the function and send results back as a tool message. This is the basis of all LLM agents and structured output workflows.
How do you reduce OpenAI API costs in production?
Cost reduction: (1) Use gpt-3.5-turbo for classification/simple tasks, gpt-4 only for reasoning, (2) Semantic caching — cache responses by embedding the query and finding similar cached queries, (3) Prompt compression — use LLMLingua to compress prompts by 3–10x, (4) Streaming — faster perceived response, (5) Batch API — 50% discount for non-realtime tasks, (6) Fine-tuning — smaller fine-tuned model can outperform GPT-4 at 10x lower cost for specific tasks.
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