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Prompt Chaining | Prompt Engineering Guide

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To improve the reliability and performance of LLMs, one of the important prompt engineering techniques is to break tasks into its subtasks. Once those subtasks have been identified, the LLM is prompted with a subtask and then its response is used as input to another prompt. This is what's referred to as prompt chaining, where a task is split into subtasks with the idea to create a chain of prompt operations. Prompt chaining is useful to accomplish complex tasks which an LLM might struggle to address if prompted with a very detailed prompt. In prompt chaining, chain prompts perform transformations or additional processes on the generated responses before reaching a final desired state. Besides achieving better performance, prompt chaining helps to boost the transparency of your LLM application, increases controllability, and reliability. This means that you can debug problems with model responses much more easily and analyze and improve performance in the different stages that need impr

Prompting Techniques Prompt Chaining Copy page Prompt Chaining Introduction to Prompt Chaining To improve the reliability and performance of LLMs, one of the important prompt engineering techniques is to break tasks into its subtasks. Once those subtasks have been identified, the LLM is prompted with a subtask and then its response is used as input to another prompt. This is what's referred to as prompt chaining, where a task is split into subtasks with the idea to create a chain of prompt operations. Prompt chaining is useful to accomplish complex tasks which an LLM might struggle to address

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