Understand how LLMs generate language, why they are useful, and why fluent output can still be wrong.
Analogy: Think of an extremely advanced autocomplete system that can continue patterns across explanations, emails, tables, and code.
D2C: It can classify a complaint as payment, delivery, promotion, return, or technical—but it needs supplied or connected data to know live order status.
Learn how RAG retrieves relevant trusted content before an LLM answers.
Analogy: A librarian finds the correct pages first, then explains the answer rather than relying only on memory.
D2C: A return-policy assistant retrieves the latest approved policy, rule, and exception, then answers with source references.
Understand how AI agents read business data and perform approved actions.
Analogy: A waiter carries a request to the kitchen and returns with a structured result.
D2C: An order agent calls payment, OMS, inventory, and courier APIs before recommending a resolution.
Learn the components of a bounded, tool-using AI agent.
Analogy: A capable assistant checks systems, follows rules, coordinates actions, and asks approval when needed.
D2C: An order-issue agent checks payment, OMS, inventory, and courier data and routes the case safely.
Learn how permissions, approval, privacy, audit, evaluation, and incident response control AI risk.
Analogy: A bank employee has access limits, financial thresholds, approvals, and audit records.
D2C: A refund agent may recommend an action but cannot release high-value money without an authorised approver.