Claude API & Anthropic Ecosystem Interview Questions Interview Guide

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I integrated Claude for customer support automation, handling context window limits and cost optimization. I implemented prompt caching for recurring queries and achieved 60% cost reduction while maintaining quality.

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I start with clear specification of input/output format, test edge cases iteratively, use few-shot examples for complex tasks, and version prompts like code. I track prompt performance metrics over time.

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I analyzed token usage patterns and found 40% waste from verbose system prompts. I redesigned prompts to be concise yet effective, implemented batching for non-urgent tasks, reducing costs by 50%.

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Claude's 200K context window enables handling long documents, conversations, and code. I design to maximize context usage: load full documents upfront, keep conversation history, include examples. Pagination required for edge cases.

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Streaming provides low-latency responses ideal for chat UIs and real-time applications. Non-streaming is simpler for batch processing. I use streaming for user-facing features, non-streaming for backend jobs.

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Implement exponential backoff with jitter, respect rate limit headers, batch requests where possible, cache frequently used prompts. Use Redis for distributed rate limiting across services.

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Use concise prompt language, leverage system prompts for recurring context, implement prompt caching, batch similar requests, use smaller models for simple tasks, prompt compression techniques.