For those running LLMs in real production environments (especially agentic or tool-using systems): what’s actually worked for you to prevent confident but incorrect outputs?
Prompt engineering and basic filters help, but we’ve still seen cases where responses look fluent, structured, and reasonable — yet violate business rules, domain boundaries, or downstream assumptions.
I’m curious:
Do you rely on strict schemas or typed outputs?
Secondary validation models or rule engines?
Human-in-the-loop for certain classes of actions?
Hard constraints before execution (e.g., allow/deny lists)?
What approaches failed for you, and what held up under scale and real user behavior?
Interested in practical lessons and post-mortems rather than theory.
al_borland•13h ago
If I was forced to use it, I’d probably be writing pretty extensive guardrails (outside of the AI) to make sure it isn’t going off the rails and the results make sense. I’m doing that anyway with all user input, so I guess I’d be treating all LLM generated text as user input and assuming it’s unreliable.
kundan_s__r•13h ago
The worst failures I’ve seen happen when teams half-trust the model — enough to automate, but still needing heavy guardrails. Putting the checks outside the model keeps the system understandable and deterministic.
Ignoring AI unless it can be safely boxed isn’t anti-AI — it’s good engineering.