Few-Shot Learning
This tutorial explores the cutting-edge techniques of Few-Shot Learning and In-Context Learning using OpenAI's GPT models and the LangChain library. These methods enable AI models to perform complex tasks with minimal examples, revolutionizing the way we approach machine learning problems.
Traditional machine learning often requires large datasets for training, which can be time-consuming and resource-intensive. Few-Shot Learning and In-Context Learning address this limitation by leveraging the power of large language models to perform tasks with just a handful of examples. This approach is particularly valuable in scenarios where labeled data is scarce or expensive to obtain.
What you'll learn
- 1OpenAI's GPT Models: State-of-the-art language models that serve as the foundation for our learning techniques.
- 2LangChain Library: A powerful tool that simplifies the process of working with large language models.
- 3PromptTemplate: A structured way to format inputs for the language model.
- 4LLMChain: Manages the interaction between the prompt and the language model.
About this tutorial
This hands-on Jupyter notebook is part of Prompt Engineering, a free open-source repository by Nir Diamant covering prompt engineering techniques with runnable code examples and detailed explanations.
Prompt Engineering: Zero to Hero
The expanded book version of this repo: 22 prompt-engineering techniques explained in depth, with hands-on exercises that take you from fundamentals to advanced steering.
Get it on AmazonKindle $9.99 · Paperback $24.99 · Free with Kindle Unlimited