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Automate customer support with AI

End-to-end path: understand what LLMs can do, write the prompts, ground on your KB with RAG, design the agent + escalation, deploy with guardrails, measure ROI.

LLMs for Business — what they can and can't do

🤖 AI in Practice · A grounded view of where Large Language Models actually excel, where they fail, and the myths worth dispelling before committing any project.

Prompt Engineering — the practical framework

🤖 AI in Practice · Structured prompting pays off. Role + task + context + constraints + format + examples = reliable outputs instead of dice-rolls.

RAG — Retrieval-Augmented Generation

🤖 AI in Practice · Give an LLM access to your own documents at query time. The most effective pattern to get accurate, grounded answers on your data.

Customer Support Automation

🤖 AI in Practice · Deflect FAQs with RAG, triage tickets with classifiers, pre-draft responses for agents. Done right, it cuts resolution time 30-50% without killing customer satisfaction.

AI Risks — hallucinations, prompt injection, privacy

🤖 AI in Practice · The three big classes of risk on production AI systems, and the practical mitigations that actually work.

AI ROI & Metrics — how to measure real value

🤖 AI in Practice · Most AI projects fail on measurement, not tech. Here's what to track to know if it's actually working and to justify scaling the budget.