Explains what AI hallucinations are, why they happen, and how to detect them by asking the same question twice.
Transcript:
Hi, a quick video today in a series of videos covering terminology for artificial intelligence. When an AI chatbot gives us the answer to its question, it’s important to understand that the answer is based on a few key things. The data on which the AI was trained is important, the limitations when attempting to generate new information, assumptions made by the model or in many cases a lack of common sense reasoning. That last one’s important because many new models are attempting to resolve this reasoning issue right now. When an AI gives us back a made up response, we call that a hallucination. Let me give you an example. What’s the world record for crossing the English channel on foot? Now in this case, I’m using a model from last year with limited training data and the result is that this is extremely challenging due to the cold water from strong currents. Regardless, someone apparently did it in 1994 and it only took them 13 and a half hours. Now of course this is clearly incorrect. If I ask the same question again though of a more current reasoning model, you can see that some common sense has been applied. In this case, it’s saying that you can’t cross the English channel on foot and here are the ways that people typically get across the channel. If you’re chatting with an AI and you suspect that the answer might not be accurate, one of the best ways to check is just ask the same question again. You might get the same result, but if it’s hallucinating, you’ll get a completely new answer.
Demonstrates how 3DN Respond integrates with CRMs like Salesforce to pull customer data and personalize AI-generated responses.
Transcript:
We built 3DN response to transform customer inquiries into exceptional service experiences. We do this by streamlining, prioritizing and resolving requests across all in-bout communication channels. This empowers your team to deliver faster, more personalized port and build lasting relationships. And today, I want to show you how we personalize responses based on your CRM. First, let me explain how we connect with your CRM system to create truly personalized interactions. We feature a flexible integration layer that currently supports Salesforce, Pype Drive, IMS and Microsoft Dynamics, and we can add a custom CRM also. The real magic happens to a powerful field mapping system. Rather than creating a rigid, one-size-fits-all integration, 3DN response allows you to specify exactly which fields from your CRM shouldn’t be included in your responses, and what they should be called. For example, if your Salesforce instance has a custom field called CPD status, you can map that into 3DN response so that it can be referenced in your response templates. When a customer inquiry comes in, the system first identifies the customer’s email address and then checks if you have an active CRM connection, and uses that to look up the customer’s full profile in your CRM. The system then applies your field mappings to extract and transform just the data you need. For instance, it might pull the customer’s name, account status, membership level, purchase history, training data and CPD information. And any other fields that you’ve specified. This rich data then becomes part of the context used by the AI when generating responses. In your props and intent templates, you can reference this customer information. And what makes this powerful is that the AI can now craft truly personalized responses. The system tracks each step of the process, recording whether the look up was successful and what data was retrieved, giving you full visibility into how the customer information is being used in responses. This integration creates a stainless connection between your existing customer data and your automated response system, ensuring that customers feel recognized and valued, not like they’re talking to a generic system that there’s nothing about them.
