Prompt Chaining
This tutorial explores the concepts of prompt chaining and sequencing in the context of working with large language models. We'll use OpenAI's GPT models and the LangChain library to demonstrate how to connect multiple prompts and build logical flows for more complex AI-driven tasks.
As AI applications become more sophisticated, there's often a need to break down complex tasks into smaller, manageable steps. Prompt chaining and sequencing allow us to guide language models through a series of interrelated prompts, enabling more structured and controlled outputs. This approach is particularly useful for tasks that require multiple stages of processing or decision-making.
What you'll learn
- 1Basic Prompt Chaining: Connecting the output of one prompt to the input of another.
- 2Sequential Prompting: Creating a logical flow of prompts to guide the AI through a multi-step process.
- 3Dynamic Prompt Generation: Using the output of one prompt to dynamically generate the next prompt.
- 4Error Handling and Validation: Implementing checks and balances within the prompt chain.
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.
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