Context engineering is the practice of carefully constructing what an AI model “sees” before generating a response: the system prompt, the retrieved knowledge-base content, the authenticated customer’s account data, tool definitions, and conversation history. For AI customer service portals, context engineering is often the difference between an AI that resolves real customer questions and an AI that hallucinates plausible-but-wrong answers.
Effective context engineering for customer portals includes: per-customer context (what plan, what orders, what tickets), authenticated state (the AI only sees what this customer is authorized to see), grounded knowledge (RAG over the help center), tool schemas (what actions the AI can take), and clear escalation rules.
See our AI customer service portal article.