7 Controls for Reducing AI Hallucinations in Production
Prompts help demos. Production needs controls: grounding, retrieval, citations, tool calls, testing, and monitoring to reduce hallucinations and risk.
Read article →Category · 5 articles
AI operations
Keeping AI systems reliable, safe, and affordable after launch — evals, drift, hallucinations, and cost control in plain language.
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7 Steps for LLM Model Performance Evaluation That Holds Up
A practical, business-first approach to LLM model performance evaluation: metrics that matter, human checks, real-user testing, and production monitoring.
Read article →10 Ways to Control LLM Costs in Production
Pilot costs lie. Learn the real LLM cost drivers in production and the practical levers to cut tokens, retries, and operational overhead without hurting quality.
Read article →8-Step AI Evals for Product Managers Playbook
A practical PM framework to define success, test AI with realistic data, set ship gates, and keep quality, safety, UX, and cost under control.
Read article →10 Metrics for LLM Output Evaluation That Leaders Can Trust
A practical, repeatable approach to LLM output evaluation that turns “seems good” into measurable criteria you can govern and improve over time.
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