Member-only story
What is Prompt Chaining, and How to Use It?
Ever tried assembling complicated furniture without instructions? Well, if yes, you probably know how something important can get missed, and the resulting product comes out wobbly. The same goes for requests made to large language models, such as GPT-3 or GPT-4, for the most complicated tasks. Without structured guidance, they may give too broad answers, lack depth, or omit critical details. It is at this point that prompt chaining comes into play — a technique in structuring the reasoning process for better results that are more accurate and comprehensive.
In this tutorial, we’ll explore what prompt chaining is, how to do it, and how to leverage it to improve the performance of LLMs on various applications.
What Is Prompt Chaining?
Prompt chaining involves the process whereby the output of one prompt becomes the input for another in some sequence. This technique breaks down the complex tasks into smaller, manageable, and easy-to-deal-with subtasks, with which the LLM can focus with relative ease. By guiding the model through a series of logically connected prompts, prompt chaining improves clarity, accuracy, and depth in the final output.
Benefits of Prompt Chaining
Breaking down complexity into simpler subtasks allows an LLM to handle each step involved in the task more manageably.
Enhances accuracy: Incorporation of intermediate steps could also result in a response that is more accurate and contextually relevant from the LLM.
Improving explainability: steps of the chain would be transparent; hence, by understanding how each step of the chain was obtained it should be easier to understand how the final conclusion was arrived at.
How to Use Prompt Chaining
Prompt chaining works by systematic decomposition of the complex task and then guiding the LLM through a series of well-set steps.
