Understanding Hallucinations: What are they?

In simple terms, hallucinations are when these big-brained AI models, known as LLMs, throw us a curveball. They might say something that's off the mark, completely make-believe, or just doesn't make sense in the grand scheme of things. It's like they have a momentary lapse in AI judgment. Here are a few types of oopsies they might make:

• Mixing up stories: Imagine the AI saying, "I had pancakes for breakfast," and then later claiming, "I skipped breakfast today." A classic case of the AI getting its wires crossed.
• Going against the grain: Say you ask the AI to praise your new tech gadget, but instead, it goes, "That gadget is super glitchy and totally overpriced." Talk about a backstabber, right?
• Getting facts wrong: It's like the AI telling you, "The eiffel tower is in Rome." Clearly, someone didn't pay attention in geography class!
• Random ramblings: This is when the AI starts talking about things that have no business being in the conversation. Like, "Elephants are grey. Also, did you know that tomatoes are technically fruit?"
  
  

Now, this can be a real headache, especially if you're trying to use AI for something serious, like sorting out insurance claims or helping customers. You need your AI to stick to the facts, not go off on some wild tangent. So, let's dive into how we can keep these hallucinations to a minimum and make sure our AI is on point.

How are we solving this?

Let's face it, we've all been there. You think your friend or colleague is the go-to guru on a particular topic, so you reach out to them for some quick insights. Then, down the line, you discover, oops, their info wasn't quite on the money. It's a common experience, right? It's like trusting your GPS, only to end up at a dead-end street.

And think about those times you've called up customer support. You're hoping for that golden answer to your problem. Most of the time, those support heroes nail it, but there's always that slim chance of getting a not-so-right answer. It's rare, sure, but hey, they're human too, and mix-ups are part of the game.

So basically, “hallucinations” (the non-medical way) are also happening in normal life, so should we just learn how to work with GenerativeAI instead and always keep our own brains working and keep the human in the game?

The point is that the colleague or customer service agent you had the ‘incorrect’ experience with learned something and it would not per se happen again. 

Alright, let's get down to brass tacks. What really counts is how well your product is working for what you need it to do. Keep an eye on the hot topics and questions that your users are buzzing about. What kind of feedback are they dropping? Are they feeling good about the chat, or are they getting a bit miffed?

Now, let's talk about when things get a bit wonky. Inaccuracy can pop up in a few ways:

• Maybe your AI is getting the wrong end of the stick with what users are saying.
• Or it could be jumping to the wrong conclusions about what your business offers.
• Or, who knows, it might be making some big promises that it just can't keep.

When you spot these hiccups, it's time to put on your problem-solving hat. Maybe you need to tweak how you're talking to your AI (that's prompt engineering for you) or give it a boost by hooking it up to your company's brain trust (yep, that's the RAG). Sometimes, you might have to set some boundaries on what your AI can do or blab about.

Understanding the chatter between your users and your AI is like having a secret weapon for crafting a top-notch AI product. And hey, that's where we at Langwatch.ai come in. We're building this nifty platform to give you a crystal-clear view of all that back-and-forth.