We recently deployed a RAG-based AI chatbot to handle first-level technical support, but we are seeing cases where it "hallucinates" or makes up non-existent software patches. It's becoming a liability for our brand reputation. Does anyone have a proven workflow or specific guardrail strategy using Deep Learning frameworks to stop these hallucinations before they reach the user?
3 answers
The most effective way I’ve found to mitigate this is by implementing a multi-layered validation system using a "Refiner" model. Basically, you pass the generated output through a second, smaller LLM that is specifically trained to cross-reference the answer against your verified knowledge base. If the confidence score is low, the bot should trigger a fallback to a human agent instead of guessing. We also started using "Citation-only" constraints where the bot is strictly forbidden from generating any sentence that doesn't have a direct vector match in the documentation. It has reduced our error rate by about 75% in the last quarter.
Have you looked into NeMo Guardrails or similar open-source libraries to set programmable boundaries for your LLM?
Focus on the temperature settings in your API calls; keeping it below 0.2 usually helps minimize the creative "drifting" that leads to those hallucinations.
I totally agree, Linda. Lowering the temperature is the first line of defense. I’d also add that refining your system prompt to explicitly say "if you don't know, say you don't know" is a simple but powerful fix.
Mark, we actually tried NeMo, but found the latency was a bit high for real-time chat. For anyone following this, I recommend looking into 'Semantic Rubrics' for your evaluation step. It allows you to define specific rules for 'truthfulness' that the model must pass. If you're using a Deep Learning approach, you can actually fine-tune a classifier to detect 'uncertainty' in the latent space of the model before the text is even fully decoded.