Guardrails can make internal LLM apps slower and less useful
An internal LLM app has reached the point where adding is taking more effort than building new features. The examples are a support bot that drafts replies using ticket history and FAQ content, and an internal helper that queries a data warehouse through an API while also answering questions over contracts and policy documents.
When the are too strict, normal work requests get rejected and employees cannot finish their tasks. When the rules are too loose, security worries about internal data leaking, and legal worries about strange answers reaching customers.
The tried methods include prompt instructions for safety and tone, role-based limits on tools and tables, regex filters for obvious problems, and stricter review and refusal settings. In practice, the support bot rejects ordinary refund and cancellation requests, the analyst helper loses context because too many data columns are blocked, and the extra checks add enough delay that people return to their old workflow.
Key points
- Safety work is taking more time than feature work in an internal LLM app.
- The support bot drafts customer replies from ticket history and FAQ content.
- The internal helper uses a data warehouse API and document question answering over contracts and policies.
- Strict rules block normal work, while loose rules raise security and legal concerns.
- Extra checks increase delay enough that users go back to older .