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AI Prompt Chaining: Techniques for More Powerful AI Workflows

Authors
  • Name
    Callum van den Enden
    Twitter

Overview

This article explores AI prompt chaining techniques that allow developers to create more powerful and nuanced AI workflows by combining multiple prompts. We'll cover key concepts, best practices, and real-world examples.

What is Prompt Chaining?

Prompt chaining is a technique for breaking down complex AI tasks into a series of smaller, more focused prompts. Rather than trying to get an AI model to handle a complex task in one go, prompt chaining allows you to guide the AI through a step-by-step process.

The key benefits of prompt chaining include:

  • Improved accuracy and reliability of outputs
  • Ability to handle more complex, multi-step tasks
  • Greater control over the AI's reasoning process
  • Easier error checking and iteration

Key Prompt Chaining Techniques

Sequential Chaining

The most straightforward approach is to string multiple prompts together in sequence. Each prompt builds on the output of the previous one.

For example:

  1. Prompt 1: "Summarize the key points of this article"
  2. Prompt 2: "Based on that summary, what are 3 follow-up questions we could ask?"
  3. Prompt 3: "Write an email to the author asking those questions"

Branching Chains

This technique uses conditional logic to determine the next prompt based on the output of the previous one. It allows for more dynamic and context-aware workflows.

For example:

  1. Prompt 1: "Analyze the sentiment of this customer review"
  2. If positive sentiment: "Generate a thank you response"
  3. If negative sentiment: "Draft an apology and offer a discount"

Recursive Chains

Recursive chains feed the output of a prompt back into itself, allowing for iterative refinement of results.

For example:

  1. Prompt: "Write a short story about a robot"
  2. Analyze the story and suggest improvements
  3. Incorporate those improvements and repeat steps 1-2 until satisfied

Human-in-the-Loop Chains

Some workflows benefit from human review and input at key decision points. This allows for quality control and customization.

For example:

  1. AI generates product description
  2. Human reviews and approves or suggests changes
  3. AI refines based on human input

Best Practices for Prompt Chaining

  1. Start simple: Begin with basic chains and gradually increase complexity.

  2. Be specific: Provide clear, detailed instructions in each prompt.

  3. Use context: Pass relevant information from previous steps to maintain coherence.

  4. Test thoroughly: Experiment with different chain structures to find what works best.

  5. Monitor outputs: Implement checks to catch errors or unexpected results.

  6. Refine iteratively: Continuously improve your chains based on results.

Real-World Applications

Prompt chaining is being used in a variety of industries and use cases:

  • Customer service: Automating multi-step support processes
  • Content creation: Generating and refining marketing materials
  • Data analysis: Breaking down complex queries into manageable steps
  • Product development: Ideation and design iteration workflows

I use this myself for certain customer emails - the first part analyses their email, sentiment, and needs with reference to a knowledge base I compiled and passes its output into a second prompt to generate the final email.

What this does is take the burden off the second AI call - effectively I'm asking each of them to do half as many things compared to it being in a single call. This means there's less chance it can go wrong, and it offers the second call a chance to correct issues from the first.

Tools and Platforms

Several platforms are emerging to help developers create and manage AI workflows:

  • NVIDIA NIM Agent Blueprints
  • Asana AI Teammates
  • OpenAI Function Calling
  • Langchain

These tools provide pre-built components and visual interfaces for constructing prompt chains without needing to code everything from scratch.

The Future of AI Workflows

As AI models become more sophisticated, we can expect prompt chaining techniques to evolve as well. Some potential developments include:

  • More advanced branching and decision-making capabilities
  • Improved integration with external data sources and APIs
  • Enhanced tools for visualizing and debugging complex chains
  • Standardization of best practices and design patterns

Personally I am really in need of a platform that can help me manage, test, and iterate on prompts with variables and a knowledge base. I've briefly looked at Prompt Hub, and although it looks great I haven't found it quite right for my needs, though it might be right for you - it is powerful software.

As you explore prompt chaining, remember that it's as much an art as it is a science. Experimentation and iteration are key to finding the right approach for your specific use case.