Tag: production-ai

5 articles

• LLM Content Safety in Production: Reducing Over-Moderation with Layered Detection, Threshold Gray-Scaling, and Human Review

  A practical guide to building a production-grade content moderation system for LLM apps. Covers input/output/tool-result detection, threshold gray-scaling, false-positive reduction loops, audit logging, and rollout gating to balance safety and user experience.

  07/08/2026 LLM Safety / Content Moderation / Guardrails / AI Governance / Production AI / RAG Safety / Agent Safety

• LLM Reranker Production Evaluation: Guarding RAG Retrieval Quality with Golden Query Sets, nDCG, and Latency Budgets

  A deep dive into production evaluation methodologies for LLM Rerankers in RAG systems, covering golden query set construction, nDCG and MRR ranking metrics, candidate set budget optimization, latency and cost governance, canary release gates, and rollback strategies to prevent retrieval ranking degradation.

  07/07/2026 LLM / RAG / Reranker / Retrieval Evaluation / Production AI

• LLM Agent Durable Workflow Production Guide: Running Long Tasks with Checkpoints, State Machines, and Human Review Recovery

  This article explains how to transform long-running LLM Agent tasks from one-shot scripts into recoverable workflows, covering checkpoints, state machines, human-in-the-loop interrupts, retries, timeouts, idempotent side effects, and a production readiness checklist.

  07/05/2026 LLM Agent / Durable Workflow / LangGraph / Temporal / Production AI

• LLM Output Safety in Production: Reducing False Positives with Tiered Review and Gray Thresholds

  A systematic guide to production-grade LLM content safety: tiered review, gray thresholds, human-in-the-loop, and false positive mitigation to balance risk and user experience.

  07/04/2026 llm-safety / content-moderation / production-ai / guardrails / tiered-review / gray-threshold

• LLM Fine-Tuning Data Governance: Stabilizing Model Quality with Versioning, Deduplication, and Contamination Detection

  An engineering deep dive into governing LLM fine-tuning datasets as production assets. Covers data version tracking, multi-level deduplication, train/validation contamination detection, quality gates, and model release rollback for trustworthy, reproducible, and auditable management.

  07/03/2026 LLM Fine-Tuning / Data Governance / Data Versioning / MLOps / Production AI