Practical, no-fluff guides to making AI chatbots, AI agents and n8n/Make automations reliable.
A practical, repeatable process to test whether your AI chatbot gives correct answers: build a test set from real questions, grade answers against your own docs, and monitor quality every day.
What AI chatbot hallucinations look like in customer service, the well-known cases that made the news, why they happen, and a practical system to catch them before customers do.
How to monitor n8n workflows in production: error workflows, execution checks, silent failures that report success, workflows that stop running, and alerts in Slack, email or WhatsApp.
How to monitor Make (formerly Integromat) scenarios: error handlers, incomplete executions, silent failures, scenarios that get disabled, and alerts that reach you in time.
What can go wrong with AI agents in production and how to monitor them: loops, failed tool calls, ungrounded answers, goals not met, slow runs and cost spikes.
A practical quality checklist for AI customer-service chatbots: knowledge base, accuracy, escalation, safety, privacy, performance and ongoing monitoring.