In our last post we looked at LLM hallucinations - when a language model confidently gives false answers. Here we will examine a different class of risk: when a customer-facing assistant strays from its core purpose, engaging in irrelevant or risky topics. This is a question of domain misalignment or intent leakage rather than model decay — it threatens brand safety, wastes compute, and undermines trust.
Amazon’s Rufus AI shopping assistant launched last year offers a cautionary example of where this risk can lead to issues . Reports surfaced of users coaxing Rufus into political discussions or even code generation — exploits likely enabled by using a generalist LLM without sufficiently strict topic constraints. This story underscored how even technically sophisticated and capable systems can fall down if consumer usage is left unchecked.
The crux of the issue here is not the AI getting its facts wrong, it’s that it might offer a beautifully reasoned answer to a question you never intended it to entertain.
But this risk is not insurmountable. With a modest investment in intent control, brands can preserve the fluid, helpful nature of an LLM-powered assistant while keeping it squarely in your domain. Below are a some examples of easy-to-implement guardrails and industry best practices for aligning an assistant to your business’s goals.
Core guardrail strategies for mitigating misalignment risk
1. Prompt / input intent gating
Before sending a user’s question into your LLM pipeline, run a quick semantic check: compare the embedding of the prompt against a “domain of intent” space (for example, product requests, comparisons, shopping guidance). If the similarity score is too low, politely refuse or redirect:
I’m optimised to help with product or shopping queries. Could you rephrase your question around your purchase or product interest?
This blocks casual attempts to hijack your assistant into unrelated domains, conserves compute, and sets expectations clearly for users.
2. Output filtering or verification
Even with a valid prompt, the model might drift in its response. After generation, run a second check: score the response’s semantic affinity to your allowed domain. If it falls below threshold, suppress or replace it with a fallback:
I’m sorry, I don’t have a confident answer for that.
Alternatively, re-prompt or regenerate with stricter constraints. This gate ensures your assistant cannot leak into unwanted topics, even when the model is tempted.
Complementary best practices to reinforce safety
While input/output gating forms the backbone, a few additional layers greatly strengthen your protection:
A. Scoped system prompts and refusal policies
Embed a clear policy in your initial system message: define the assistant’s identity, allowed scope, and a refusal strategy for disallowed topics. This “soft boundary” helps the model self-regulate before your downstream checks catch issues. But on its own it is not enough — it must be paired with runtime checks.
B. Rule-based topic filters and deny lists
Maintain a list of forbidden content types (e.g. coding instructions, legal advice, politics, violence). Use keyword, pattern, or semantic filters on both user inputs and candidate outputs. Many modern guardrail toolkits support this “deny list” enforcement. (These filters help with fast, deterministic blocking.)
C. Runtime constraint frameworks
Tools like NeMo Guardrails, Guardrails AI, or emerging systems like LlamaFirewall provide programmable, processable rails around your LLM. They let you embed domain constraints, define conversation flows, and monitor compliance in real time. (For example, LlamaFirewall has been proposed as a security guardrail layer designed to detect prompt injections or prevent unsafe code generation.)
Final Thoughts
Domain misalignment is a subtle but pervasive threat — it quietly erodes brand trust, burdens systems, and confuses users - but it can be tamed from day one with layered intent controls. The strategies above are already in practice among AI-savvy organisations. Semantic gating is lightweight; rule filters and denial lists are deterministic; and runtime guardrail frameworks formalise control.
That said, there is no perfect guardrail, stronger security often comes with lower flexibility. The goal is not to lock down the assistant into rigidity — it is to let it run freely within safe lanes. With these layered measures, you can deploy conversational assistants confidently, knowing the risk of domain leakage, off-topic distraction or brand misalignment is much reduced.
