What is Prompt Chaining

Prompt chaining is a natural language processing (NLP) technique used to tackle complex, multi-step tasks with generative AI models like ChatGPT. Instead of asking the AI to complete the entire task in one prompt, the task is broken down into smaller, more manageable steps. Each step in the chain uses a separate prompt, with the AI's output from one step informing and feeding into the next step in the sequence. This allows the AI to focus on one sub-task at a time and progressively build up to completing the overarching complex task.

Some common applications of prompt chaining include content creation, strategic planning, and designing training programs. For example, if generating an employee briefing on AI automation, the first prompt could be to create an outline covering key topics like benefits, tools, concerns and next steps. Subsequent prompts would then dive deeper into each outlined topic, like asking the AI to list common employee concerns about AI automation or ways to mitigate data security risks. By the end of the chain, the AI will have generated in-depth, well-structured content for the full briefing. Similarly, for strategic planning, an initial prompt could generate an outline of a marketing plan, with follow-up prompts expanding on each component like target audience, marketing goals, tactics, budgets, etc.

It's important to distinguish prompt chaining from the related technique of chain-of-thought (CoT) prompting. While CoT prompting also walks an AI through steps to solve a task, it does so in a single prompt by asking the AI to explain its reasoning. In contrast, prompt chaining uses multiple prompts in a sequence, with each building successively on the output of the last. However, both techniques aim to coax more detailed, relevant, and structured responses from AI compared to single-shot prompting. Ultimately, prompt chaining is best suited for complex tasks requiring multiple distinct reasoning steps, allowing generative AI to demonstrate its full potential.