Have you ever wondered how to get more accurate and tailored results from a Large Language Model? 🤔💬
Enter the world of in-context learning. In simple terms, in-context learning allows a model to understand and adapt based on the information you feed it. The more context you provide, the more refined the output. 🌐🧠
So, where does prompt engineering fit in? Think of prompt engineering as the art of crafting these pieces of context in a way that guides the model toward the desired outcome. It’s essentially in-context learning itself and your direct channel of communication with the model. You can present your problem statements in two broad ways—either with minimal context, known as zero-shot or one-shot prompts, or with additional guiding context, called few-shot prompts. 🚀🤓
However, for now, you can park the jargon and realize that each prompting approach has its strengths and limitations, but the aim is the same: to pull the most precise responses out of the LLM. 🎯💡
Next Lesson 📖👣🔜