Ever asked ChatGPT a question and gotten an answer so confidently wrong that you actually had to laugh out loud? Yup, same! It’s called an AI hallucination. Unlike the human kind (which might involve sleep deprivation and hard drugs), ChatGPT’s hallucinations happen for a few key reasons.

AI prompting is a powerful technique that influences the way generative models produce responses. By carefully structuring prompts, users can optimize the model's performance for different tasks. In this blog, we explore various prompting strategies and how they shape AI outputs, along with examples of both ineffective (bad) and effective (good) prompting.

Prompting is just a fancy word for “telling an AI what to do” in a way that makes sense to both you and the machine. Think of it like talking to a very literal genie—what you say determines what you get. The more detailed your prompt, the better the response.

Okay, buckle up, because we’re about to go on a journey through the magical world of Large Language Models (LLMs)—but don’t worry, I’m keeping this at the level of ‘barely awake before coffee’ comprehension.

DeepSeek, a new player in the artificial intelligence industry, has burst onto the scene, and let’s just say, it’s got everyone—from tech giants to casual AI users—talking. This Chinese AI startup hasn’t just disrupted the industry; it’s thrown a curveball at Nvidia and stirred up a pot of questions about technology, trust, and the future of AI. Let’s dive into why DeepSeek is either the hero we didn’t know we needed or the villain we never saw coming.

Welcome to 2024, where the world of AI prompt engineering is buzzing with innovation and excitement! If you're ready to dive into the art of crafting perfect prompts to get the best results from AI models, you're in the right place.