
LLM prompting guide
[[open-in-colab]]
Large Language Models such as Falcon, LLaMA, etc. are pretrained transformer models initially trained to predict the 
next token given some input text. They typically have billions of parameters and have been trained on trillions of 
tokens for an extended period of time. As a result, these models become quite powerful and versatile, and you can use 
them to solve multiple NLP tasks out of the box by instructing the models with natural language prompts.
Designing such prompts to ensure the optimal output is often called "prompt engineering". Prompt engineering is an 
iterative process that requires a fair amount of experimentation. Natural languages are much more flexible and expressive 
than programming languages, however, they can also introduce some ambiguity. At the same time, prompts in natural language 
are quite sensitive to changes. Even minor modifications in prompts can lead to wildly different outputs.
While there is no exact recipe for creating prompts to match all cases, researchers have worked out a number of best 
practices that help to achieve optimal results more consistently. 
This guide covers the prompt engineering best practices to help you craft better LLM prompts and solve various NLP tasks. 
You'll learn: