What is Prompt Chaining? Check Its Definition, Example and Related Tools

The paradigm shift to strong AI, referred to as prompt chaining, has brought about new ways to interact with generative models. With prompt chaining, users tweak and enhance initial outputs using carefully staged prompts, converting complex tasks into simple subtasks, and considering the use of large language models (LLMs). This approach has received a lot of attention in natural language processing (NLP) because it can improve the quality and controllability of text generation. Its uses are content creation, software development, product design, and strategic planning.

Prompt chaining is a trick in which the consequence of one prompt is used as input for another. This tactic works best on tasks that are complicated and need to be built upon from their starting points. It involves breaking down work into smaller, more manageable sections, where the output of one section becomes the input of the next. Use cases include content creation, product design, software development, strategic planning, and others. 

Because large language models (LLMs) can preserve context and improve on previously generated outputs, they are often applied to LLMs, according to (Chen et al., 2021). Prompt chaining implementation is facilitated by FormWise and OpenAI’s Dall-E picture-generating model, among other tools, making this a powerful tool in AI.

In Short

• Definition and Function: Prompt chaining involves using the output of one AI prompt as the input for another, streamlining complex tasks into manageable steps.
• Use Cases: Widely applied in content creation, software development, product design, and strategic planning, enhancing efficiency and precision.
• Top Tools: Prominent tools include OpenAI GPT, Chainer, Hugging Face, Rasa, and LangChain, each offering unique capabilities for implementing prompt chaining.

What are the tools for prompt chaining?

Prompt chaining is a process used in natural language processing to complete hard tasks. The table below lists the five highest-ranked prompt-chaining tools:

Tool	Description
OpenAI GPT	For the assumption of the human-like text, advanced languages are helpful in various NLP tasks.
Chainer	This is a library for constructing and executing chains of neural networks, which is useful for prompt chaining workflows.
Hugging Face	These have multiple pre-trained models and tools available for NLP, including prompt chaining.
Rasa	An open source conversational AI framework that supports chaining multiple dialogue prompts.
LangChain	The framework has been made to create applications with large language models by use of composable components.

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How does Prompt Chaining Work?

The usual procedure is as follows:

• Giving an initial clue: This describes the main aim or task so that the model is guided in what to say.

• Analyzing the response: checking the response and identifying areas that require clarification or improvement so that it is correct.

• Coming up with another prompt: Address specific challenges or seek additional information that will help focus the answer.

• Repeat steps 2 and 3: Until the required precision/detail is attained, insisting on relevance and focusing on the models’ outputs.

Using prompt chaining, users can guide the reasoning process of the model, leading to more accurate and faster outputs. This approach has proved particularly effective in solving problems, planning tasks, or conducting open-ended research that needs detailed examination. Users can eventually get their desired output by constantly revising their prompts, thereby getting more specific responses from the model.

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Image Source: Cobus Greyling

Case Studies

Case Study 1: Visualization Code Debugging

This case study focuses on debugging visualization code using a method called “Chaining.” The process involves breaking down complex tasks into smaller, manageable steps. For instance, when a data visualization has an error (like using circle size to represent a category, which is incorrect), there are multiple ways to fix it. The Chain helps by first converting the code into easy-to-understand language, checking for design issues, and then suggesting fixes. This method proved more effective than a single run of an AI model, as it provided clearer and more accurate solutions for the errors.

Key Points:

1. Step-by-Step Approach: The Chain breaks down tasks into smaller steps for better accuracy.
2. Multiple Solutions: It offers various ways to fix visualization errors, improving flexibility.
3. Improved Accuracy: More effective than a single AI run, providing clearer solutions.

Case Study 2: Assisted Text Entry

This case study explores how Chaining can improve assisted text entry, particularly for users who rely on gaze input or shorthand typing. The Chain allows users to type short abbreviations, which the AI then expands into full sentences. For example, typing “LTSGCHKITOT” can be expanded to “Let’s go check it out.” The Chain also helps users select from multiple possible expansions, making the process interactive and reducing ambiguity. This method simplifies complex logic into user-friendly interactions, making text entry faster and more efficient.

Key Points:

1. Interactive Expansion: Users can type abbreviations and choose from expanded phrases.
2. Reduced Ambiguity: Helps resolve confusion by offering multiple expansion options.
3. User-Friendly: Simplifies complex processes into easy interactions for a better user experience.

Case Studies Source: Research paper

Example of Prompt Chaining

Prompt chaining is a way of producing text in artificial intelligence and natural language processing by linking various prompts together.

For instance:

• Initial Prompt: “Write a narrative of a character who discovers a hidden world.”
  • Response: “She uncovered an undisclosed meadow while wandering through the woods. Suddenly, she found herself in an unfamiliar land with no parallel.”

• Following Prompt: “Describe the world she enters.”
  • Response: “This world was filled with tall trees. As far as my eyes could reach, I could see light coming from their trunks which seemed to float on air. The air smelt of flowers growing and there were gentle hums of wind chimes.”

• The next prompt: “What does she find in this world?”
  • Reaction: There were many animals she met each of which had their own superpower. Some beings had the power to manipulate time, others were masters of the elements.”

In this example, the first Prompt establishes the setting, and the other three establish the plot to form a multi-layered gripping tale.

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Step By Step Process of Prompt Chaining

The following is a detailed procedure for prompt chaining in bullet points:

Step 1: Objective Identification

You have to determine what exactly you need to do with prompt chaining, such as developing specific text, storytelling, or problem-solving.

Step 2: Create the Original Question

Make an opening prompt that is clear and concise while setting the context for the work. This prompt should not be misconstrued and should be informative.

Step 3: Generating Initial Response

Provide an AI response to the first prompt. This response should be relevant and make sense.

Step 4: Review and Revise

Evaluate automatically generated responses and amend them if required. It might involve rewriting, rephrasing, or adding further background information.

Step 5: Create the Next Prompt

The next question will be based on the upgraded response. This food will guide the next generation and build on the previous result.

Step 6: Repetition of Steps 3 through 5

Repeat Steps 3-5 over and over again until the desired outcome is achieved. Keep generating answers, improving on them, and creating new prompts until you get what you want.

Step 7: Assessment and Modifications

Before announcing that it is done, review the final replies and make any necessary adjustments.

Conclusion

Prompt chaining is an influential AI approach in which the desired outcome is achieved by responding to a sequence of hints. It simplifies complex tasks by breaking them down into smaller, manageable parts. This technique enhances coherence and consistency in text generation, resulting in more precise and captivating outputs. It is particularly useful when one needs to maintain a uniform tone, style, or structure.

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