What is prompt chaining and why you should start using it?

What is prompt chaining? This is a question that often arises in discussions about advanced language models and artificial intelligence.

To understand what is prompt chaining, one must first grasp the basic concept of a “prompt”. In the context of AI, a prompt is an input or a query given to an AI model, like OpenAI’s GPT-4o or Google’s Gemini, which generates a response based on the provided input. Prompt chaining, therefore, refers to the technique of linking multiple prompts together to achieve a more complex and nuanced interaction with the AI.

Prompt chaining is not just a single query and response; it involves a sequence of prompts that build on each other, allowing the AI to develop a deeper understanding and produce more refined outputs. This method has become particularly valuable as AI applications have grown more sophisticated, requiring more detailed and context-aware responses.

What is prompt chaining’s core concept?

To start with the basics, what is prompt chaining at its core? Prompt chaining involves the sequential use of multiple prompts where each prompt and its response informs the next one. This technique utilizes the contextual understanding of AI models to create a continuous and coherent flow of information.

Unlike a single prompt that might generate a brief or limited response, a chain of prompts can guide the AI through a more elaborate process of reasoning and generation.

Imagine you are trying to teach an AI about the life cycle of a butterfly. A single prompt asking “What is the life cycle of a butterfly?” might provide a concise answer, but it might not capture the intricate details you are looking for.

By using prompt chaining, you can break down the query into smaller, more specific questions, such as “Describe the egg stage of a butterfly,” followed by “What happens during the larva stage?” and so on. Each prompt builds on the information provided by the previous one, resulting in a comprehensive explanation of the butterfly’s life cycle.

This chaining process not only enhances the quality of the responses but also ensures that the AI model maintains context throughout the interaction. By keeping track of the sequence of prompts, the model can deliver more accurate and contextually relevant information.

How prompt chaining works

Understanding how prompt chaining works involves looking at the mechanics behind it. When an AI model receives a prompt, it generates a response based on its training data and the specific input it has received. In prompt chaining, the initial prompt sets the stage for subsequent queries. The response from the first prompt is used as context for the next prompt, creating a chain of inputs and outputs that build upon each other.

For example, consider the following sequence of prompts about planning a trip:

• Initial prompt: “Suggest a destination for a summer vacation.”
• Follow-up prompt: “What are some popular activities to do in Bali”
• Next prompt: “Can you suggest a good itinerary for a week-long trip to Bali”

Each response provides a foundation for the next prompt, creating a coherent and informative dialogue. The key to effective prompt chaining is to ensure that each subsequent prompt logically follows from the previous response, maintaining the context and continuity of the conversation.

Moreover, prompt chaining can be used in more complex scenarios where detailed analysis or step-by-step processes are required. For instance, in a technical domain, one might use prompt chaining to debug a piece of code by asking the AI model to identify errors, suggest corrections, and verify the fixes step by step.

Let’s put it to the test

Now that you know the answer to what is prompt chaining, we would like to give an example to show how big the difference is using Google’s Gemini.

Output without prompt chaining:

Output with prompt chaining:

The two outputs provided summarize the impact of climate change on polar bears, but they do so with some key differences in structure, detail, and emphasis. Here are the main distinctions:

Structure and format

1. Output without prompt chaining:
   • Detailed Breakdown: This output provides a detailed breakdown of the impacts with clear subheadings such as “Loss of Hunting Grounds,” “Reduced Body Condition,” “Increased Drowning,” etc.
   • Additional impacts and conservation efforts: It includes additional sections on “Reduced Genetic Diversity,” “Increased Exposure to Pollutants,” and “Conservation Efforts,” making it more comprehensive.
   • Bullet points: The use of bullet points for each impact makes it easier to read and navigate.
2. Output with prompt chaining:
   • Paragraph format: This output presents the information in a more continuous paragraph format, grouping related impacts together.
   • Concise summary: It provides a concise summary of each point without additional subheadings or detailed explanations for each category.

Detail and depth

1. Output without prompt chaining:
   • In-depth analysis: Offers a more detailed analysis of each impact, explaining not only what happens but also why and how it affects polar bears.
   • Comprehensive coverage: Addresses a wider range of impacts, including less obvious ones like genetic diversity and exposure to pollutants.
2. Output with prompt chaining:
   • Concise coverage: Focuses on the primary impacts in a more succinct manner, covering the essential points without as much elaboration.
   • Focused on key points: Emphasizes the most critical issues like malnutrition, cub survival, and human-bear conflict, making it shorter and more to the point.

Emphasis and tone

1. Output without prompt chaining:
   • Balanced emphasis: Gives equal weight to various impacts and also emphasizes conservation efforts, providing a broader perspective on the issue.
   • Informative tone: The tone is more educational and aims to inform the reader comprehensively about the various facets of the problem.
2. Output with prompt chaining:
   • Urgency and threat: Emphasizes the urgency of the threat and the long-term survival of polar bears more strongly, making it clear that immediate action is needed.
   • Direct tone: The tone is more direct and urgent, aimed at highlighting the severe consequences of inaction.

As you can see, if the only thing you want to learn is the impact of climate change on polar bears, using prompt chaining works better to give you the information you want accurately as the output without prompt chaining provides more detailed information with a structured format, while the output with prompt chaining offers concise yet focused points with an emphasis on urgency.

We hope we were able to explain what is prompt chaining and why you would want to use it in detail. As AI technologies continue to evolve and integrate into our daily lives, understanding and leveraging techniques like prompt chaining becomes increasingly valuable. By embracing these advancements, we can unlock new possibilities and navigate the new era of humanity.

Featured image credit: Freepik