LLMs are most useful in applications when they are given a focused task, reliable context, and clear boundaries for what they can change.
Start with a workflow
Instead of adding a chatbot without a purpose, define the repetitive workflow first: classify a request, retrieve the relevant context, generate a draft, or call a validated tool.
Keep the system observable
Log inputs, tool calls, latency, and user corrections without storing sensitive data unnecessarily. This makes quality improvements measurable and helps catch regressions.
Build for graceful failure
Every generated result should have a safe fallback. Validate structured output, limit tool permissions, and give the user a clear way to retry or take over manually.