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Build a comprehensive analytics backend for AI features. Track queries, user satisfaction, funnel conversion, and detect anomalies in AI system behavior.
Build immutable audit trails for AI systems with structured logging, compliance-ready records, and analytics for debugging and fraud detection.
Build scalable AI background processing with BullMQ, idempotent job tracking, exponential backoff, progress streaming, and webhook callbacks for reliable async workflows.
Reduce AI costs by 50% with OpenAI Batch API. Process embeddings, classifications, and reports offline with intelligent pipeline design.
Guide to building domain-specific LLM benchmarks, task-based evaluation, adversarial testing, and detecting benchmark contamination for production use cases.