How can I mitigate hallucinations in AI-generated content when using pre-trained models for customer support automation

Question

I was facing problem related to hallucinations so can you tell me with the help of python programming that How can I mitigate hallucinations in AI-generated content when using pre-trained models for customer support automation?

Answer 1

To mitigate hallucinations in AI-generated customer support content, use retrieval-augmented generation (RAG) to ground responses in factual data and enforce strict response validation.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Retrieval-Augmented Generation (RAG) – Retrieves relevant factual data before AI generation.
• Vector Search with FAISS – Matches user queries to the closest knowledge base entry.
• Grounded AI Responses – Incorporates retrieved facts into AI-generated responses.
• Low-Temperature Setting – Uses temperature=0 to enforce deterministic, non-speculative outputs.
• Preventing Misinformation – Ensures AI does not generate unsupported claims by always referencing factual sources.

Hence, mitigating hallucinations in AI-generated customer support automation is best achieved through retrieval-augmented generation (RAG), grounding responses in a structured knowledge base, and enforcing deterministic output settings.