🤖 Stop AI Hallucinations: 3 Model Customization Secrets

Key Takeaways

What did this AI model just say? That's called a hallucination. I'm going to quickly fill you in on three ways you can customize your model for more accurate answers. First up, prompt engineering. This is the quickest way to get better responses from your model. You're not changing the model itself. Instead, you're carefully crafting the input. It's like fine-tuning the question to get a better answer. However, prompt engineering is limited by what the model already knows. So, if you want something with a deeper understanding, you'll need something more robust, which brings us to fine-tuning. Now, imagine you want your model to speak finance or legal fluently. Fine-tuning actually retrains the model itself with specific data. It's like sending the model back to school for a specialized degree. And then there's rag. Instead of retraining the model itself, you access external knowledge base in real time, grabbing the latest information only when it's needed. That's perfect for dynamic fields where information changes quickly like live data or industry specifics and it pulls the data without the permanent changes to its core. These are three different ways you can customize for your use case and mitigate those hallucinations we saw earlier. To learn more, check out the link.