📄️ What is a Prompt?
A prompt in the context of Large Language Models (LLMs) is the input text or instruction given to the model to guide its response or output. It serves as the starting point or context that influences how the model generates text, answers questions, completes tasks, or performs any other functions it is designed to handle.
📄️ Intro to Prompt Engineering
Prompt engineering is a crucial aspect of working with Large Language Models (LLMs). It involves designing and crafting prompts to effectively communicate with the model, ensuring it generates useful and accurate responses. Here's a detailed guide on prompt engineering, covering its importance, types, best practices, and more.
📄️ Few-Shot Prompting
Few-shot prompting is a technique used in interacting with Large Language Models (LLMs) where the model is provided with a small number of examples (often between one and a few dozen) within the prompt itself to help guide the model's response. This approach leverages the model's ability to learn from examples on-the-fly, enabling it to generate more accurate and contextually relevant outputs based on the patterns it identifies in the provided examples.
📄️ Chain of Thought Prompting
Chain of Thought (CoT) prompting is a technique used in interacting with Large Language Models (LLMs) where the prompt explicitly encourages the model to generate intermediate reasoning steps or explanations before arriving at a final answer. This approach helps the model simulate a step-by-step thought process, leading to more accurate and explainable outputs, especially for complex tasks that require logical reasoning or multi-step problem-solving.