There are many prompt engineering techniques that make interactions with large language models (LLMs) like GPT-4, Gemini, or Claude more efficient. In today’s article, you’ll learn how to link multiple prompts together to create a seamless, step-by-step workflow for complex tasks with prompt chaining.
Prompt chaining helps maintain the context and control of the AI’s output. Instead of overwhelming the AI with a single complex prompt, you guide it through a series of simpler, connected prompts.
Each response in a chain builds on the last, which leads to more accurate and coherent answers. By mastering this technique, you can handle intricate tasks more effectively and with greater precision.
Here’s everything you need to know to get started! 👇
⚛️ What is Prompt Chaining?
Remember the scene in Harry Potter and the Sorcerer’s Stone where Hermione solves the potion puzzle?
First, she eliminates the poison.
Next, she identifies the wine.
She narrows it down to two potions. One lets them move forward. The other sends them back. Each step builds on the last. And this is pretty much how prompt chaining works.
In a nutshell, prompt chaining is an AI prompting technique where you connect multiple prompts or instructions in a sequence. This allows large language models to generate more accurate and relevant responses. It also makes it easier for AI to tackle complex tasks in bite-sized steps.
So, how does it work?
Let’s say you’re planning a new marketing campaign. This is an inherently sequential task.
You outline your campaign goals and identify your target audience. Next, you use the information to create tailored content for each marketing channel. Then, you schedule and launch the campaign across various channels, using the created content. And this is just the beginning.
If you try to throw all that at the LLM at once, it will hallucinate or lose context. But if you break the task down into compounding steps, the AI will approach the task gradually and build on its own output.
Each consecutive prompt in a chain instructs the LLM to “recycle” the result from the previous generation and incorporate it into the next. It also specifies the expected format for the output.
This approach is a more advanced version of other types of prompt engineering techniques like zero-few-shot prompting or chain-of-thought prompting. And it comes with a number of unique benefits.
➕ Benefits of Prompt Chaining
Enhanced Control
Regular prompting techniques leave a lot to chance. Even with multi-level, context-rich prompts, AI can still occasionally veer off the intended path or produce inconsistent results.
With prompt chaining, you gain enhanced control over the output by breaking the task into smaller, manageable chunks and continuously guiding the AI with clear-cut, specific instructions.
Read the full article about what is prompt chaining on the official Taskade Blog.
