9 Signs of AI Drift and How to Catch It Early
AI drift is slow performance decay in production. Spot early symptoms, understand causes, and set up monitoring that protects revenue, cost, and trust.
Read article →Category · 10 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 Simple AI Evals for Non-Engineers That Prevent Bad Rollouts
AI evals for non-engineers: define success, set guardrails, and run repeatable checks to avoid AI that “usually works” but fails on what matters most.
Read article →9 Practices for LLM App Reliability After Launch
LLM apps fail quietly: quality drifts, costs rise, trust drops. Use SLOs, observability, safety controls, and fallbacks to keep performance stable after launch.
Read article →7 Ways To Spot AI With The Highest Hallucination Rate
Learn what “AI with the highest hallucination rate” means, how teams measure it, and how to evaluate models in your real workflows before launch.
Read article →7-Step ChatGPT Prompt to Reduce Hallucinations
A practical, leader-friendly prompt framework that grounds ChatGPT in approved sources, forces uncertainty, adds traceability, and standardizes safe outputs.
Read article →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 →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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