Thank you for your thoughtful question! You're absolutely right that traditional LLMs can hallucinate, but our Knowledge Assistant is designed to minimize this issue through several safeguards:

1) Retrieval-Augmented Generation (RAG): Rather than letting the LLM invent answers from its own parameters, we use it alongside vector search. The Assistant retrieves the most relevant information from a curated knowledge base - official product documentation, FAQs, and user guides.

2) Contextual Narrowing: We operate in a domain-specific environment (ERP). This domain focus makes it easier to accurately match user questions with existing, authoritative documents. By narrowing the context, we reduce the chance for off-topic or purely speculative answers.

3) Reference and Verification: Whenever the Assistant answers a query, it can cite or reference the source from which it pulled the information. This allows users (and internal subject-matter experts) to verify the correctness of the response. It also reinforces user trust, since the reasoning path is transparent.

4) Feedback Loop and Confidence Thresholds: We incorporate feedback mechanisms allowing users to report inaccuracies. If the Assistant is uncertain about the best answer, it will either ask clarifying questions or provide relevant references to confirm. Over time, user feedback helps refine the Assistant’s accuracy.

With these safeguards, our Assistant provides reliable and accurate answers, greatly reducing errors and building user confidence. Let us know, if you have additional questions!