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Implement exact-match and semantic caching with Redis to dramatically reduce LLM API calls, improving latency and cutting costs by 60% through intelligent cache invalidation.
Detect and redact PII before sending to LLMs, pseudonymize sensitive data, and maintain GDPR compliance with privacy-preserving AI.
Comprehensive architecture for production LLM systems covering request pipelines, async patterns, cost/latency optimization, multi-tenancy, observability, and scaling to 10K concurrent users.
Treat prompts as code with version control, A/B testing, regression testing, and multi-environment promotion pipelines to maintain quality and prevent prompt degradation.
Implement token-based rate limiting with per-user budgets, burst allowances, and cost anomaly detection to prevent runaway spending and ensure fair resource allocation.