Applied AI Tools

We define prompt chaining as a prompt optimization technique to break down a big task into bite-sized steps for AI. Think of it like creating a chain reaction: the AI completes the first small step. Its answer becomes the starting point for the next step, and so on.

But, what is prompt chaining in Generative AI? Why would you do this?

Sometimes, when you ask an AI like ChatGPT a really big, complicated question, you may have experienced it doesn’t respond. Sometimes it is due to scope or content guideline issues, and sometimes it just can’t comprehend your prompt.

Even though these tools are incredibly smart, they can get a bit lost. They may miss parts of your inquiry when you ask for too much at once. It’s like trying to explain a whole multi-step project in one breath – details can easily get missed!

But what if there was a better way to guide the AI through complex tasks?

That’s where a clever technique called prompt chaining comes in.

This prompt chaining explained simply is a method to lead the AI through a process, one link at a time. This prompt optimization technique has become very important in the world of generative AI. It helps make these powerful tools more reliable for complex jobs.

Now, you might wonder, what is the use of chaining prompt commands together like this?

The primary purpose of prompt chaining is to help the AI give more accurate answers when faced with complex challenges.

Instead of hoping the AI figures everything out from one massive prompt, we guide it. This structured approach is essential in prompting engineering. It involves the skill of writing good instructions or ‘prompts’ for AI.

Ultimately, what does prompt chaining allow you to do?

It lets you use AI to tackle bigger, more sophisticated problems than you could with just single prompts.

In this blog post, we’ll dive deep into prompt chaining.

• Learn how prompt chaining works.
• Compare prompt chaining to other ways of improving AI responses.
• Learn how you can start using prompt chaining for daily productivity using real-world examples.

How does prompt chaining optimization work?

So, we know prompt chaining helps AI tackle big jobs. But how does it actually work?

At its heart, prompt chaining is about connecting instructions.

Instead of giving the AI one giant command, you give it a series of smaller commands. These commands are related and given one after the other. The real magic happens in how these commands link together. The answer (or output) from the first command becomes a key piece of information (input) for the second command. That second answer feeds into the third, and so on.

This prompt chaining technique works because it guides the Large Language Model (or LLM). By prompt chaining LLMs step-by-step, we help it stay focused and build upon earlier results. This leads to a much better final outcome for complex tasks.

Here’s a prompt chain diagram to get a gist:

Now, let’s breakdown each step shown above:

Task decomposition: Break it down

First, you look closely at the big goal you want the AI to achieve. You figure out the smaller, logical steps needed to get there.

For example, if you want the AI to write a report, the steps might be:

• Research the topic
• Create an outline
• Write a first draft
• Edit the draft

Similarly, break down the task you want the AI model to do in simple and singular steps.

Prompt design: Write the AI prompts

Next, you write a clear, specific prompt (instruction) for each of those small steps. Your first prompt might ask the AI to research, the second to outline based on the research, and so on.

Execution and linking: Connect the prompt chain

This is where the “chain” happens. You give the AI the first prompt. You take its response. Then, you use that response as part of your next prompt. For instance, you’d tell the AI: “Based on this research [insert AI’s research output here], create an outline.” You repeat this process, linking each step with the output from the earlier one.

Refine output: Get the final result

The answer from the very last prompt in your chain is usually your final result. Sometimes, you might combine outputs from a few steps to get the whole picture.

Prompt chaining vs. other prompt engineering techniques

Prompt chaining is a powerful tool in our toolkit. But, it’s helpful to know it’s not the only method out there. Understanding the differences helps you choose the best approach for your specific task. Let’s compare prompt chaining to a few other common techniques.

Prompt chaining vs. chain of thought (CoT)

You might have heard of Chain of Thought prompting, often shortened to CoT. If not, learn more here: Chain-of thought prompting for ChatGPT – examples and tips

The core idea of chain of thought prompting vs prompt chaining lies in how they handle complex reasoning.

With CoT, you ask the AI to “think step-by-step” within a single prompt. You encourage the AI to explain its reasoning process as it arrives at the final answer, all in one go. It’s like asking someone to show their work on a math problem.

Prompt chaining, as we’ve learned, uses multiple, separate prompts linked together. Each prompt handles one step, and the output feeds into the next.

Here’s a quick comparison:

• Structure: CoT uses one prompt to show internal thinking steps. Chaining uses multiple prompts for sequential external steps.
• Flexibility: Chaining often gives you more flexibility. If one step isn’t quite right, you can tweak just that specific prompt. With CoT, you usually need to adjust the entire single prompt.

When to use chain of thought prompting vs prompt chaining technique?

CoT is fantastic for tasks where seeing the logical flow is crucial. This includes solving math problems or answering complex reasoning questions. It all happens within one response.

When to use prompt chaining technique vs chain of thought prompting?

Chaining excels at managing workflows. It is effective for building content piece by piece, like drafting then editing. It suits any process where you want to control each stage and potentially adjust it.

Prompt chaining vs. simple or zero-shot prompts

For straightforward questions (“What time is it in Mumbai?”), a single, simple prompt works perfectly.

Asking a single prompt to do something really complex can be challenging for the AI. For example, if you ask, “Write a detailed report on climate change impacts in India.” Then, suggest solutions, draft a presentation, share content distribution strategy – all in a single prompt. The AI might struggle with this. It might miss steps, forget details, or produce a lower-quality result. Chaining breaks this complexity down, making it manageable for the AI.

Prompt chaining vs. AI agents

You might also hear about AI Agents. Think of Agents as more advanced AI systems designed to act more independently to achieve a goal. I have published an interesting article on how Vertical AI agents will replace SaaS.

AI Agents can often make plans. They decide which tools to use, like searching the web or running code. They execute steps autonomously. So, choosing between prompt chaining vs agents isn’t really an either/or situation.

An AI Agent is a type of system. It might use prompt chaining internally. This strategy helps break down complex tasks. These tasks are necessary to reach its overall goal.

Chaining is a prompting method an Agent might use.

Prompt chaining vs. prompt tokens

It’s also important to distinguish prompt chaining vs prompt tokens.

Tokens are the basic units of text that AI models process. You can think of them roughly as words or parts of words. The length of your prompts and the AI’s answers are measured in tokens. This often relates to how much using the AI costs.

Prompt chaining is about how you structure multiple prompts. Tokens relate to the size and cost of each message exchanged with the AI. They are fundamentally different concepts.

Separate chaining vs linear probing

Separate chaining and linear probing might sound related. But they belong to a completely different area of computer science dealing with data structures. It is called hash tables, a technical way to organize data. They have nothing to do with how we prompt Large Language Models. So, if you see these terms, know they refer to something else entirely!

Types of prompt chaining structures

While a simple step-by-step chain is common, prompt chains can be structured in many clever ways to handle different situations:

Sequential prompt chaining

This is the basic type we’ve discussed most. One step follows another in a straight line, ideal for linearly progressive tasks.

Prompt A’s output feeds into Prompt B, Prompt B’s output feeds into Prompt C, and so on.

Sequential prompt chaining example:

Summarize meeting notes -> Extract action items from the summary -> Format action items into an email.

Branching prompt chaining

Think of this like a fork in the road. The output from one prompt is sent to multiple different prompts or chains that run in parallel. This is great to work on multiple scenarios to get the outputs with few prompt chains.

Branching prompt chaining example:

Main branch: Analyze customer survey feedback -> (Branch 1). Using the analysis, find outputs for these:

• Find key complaints -> (Branch 2)
• Extract positive comments -> (Branch 3)
• Calculate an overall satisfaction score -> (Branch 4)

Iterative prompt chaining

This involves repeating a prompt or a series of prompts until a certain condition is met. It’s like refining something until it’s just right.

Iterative prompt chaining example:

Draft a marketing slogan -> Use another prompt to rate the slogan’s catchiness (e.g., score 1-10)

• If the score is below 8, use the rating feedback to draft a new slogan
• Repeat until the score is 8 or higher.

Hierarchical prompt chaining

Here, you break a large task into main tasks and related sub-tasks, like an organizational chart. The results from the lower-level sub-tasks feed into the higher-level main tasks.

Hierarchical prompt chaining example:

Goal: Create a business plan (Main Task):

• (Sub-task 1) Analyze market
• (Sub-task 2) Develop marketing strategy
• (Sub-task 3) Create financial projections
• (Sub-task 4): Combine analysis, strategy, and financial into the final business plan document.

Conditional prompt chaining

This prompt chain type makes it easy for finalizing decisions. Based on the output of one prompt, the chain dynamically chooses which prompt comes next. It’s like using “if… then…” logic.

Conditional prompt chaining example:

Scan an incoming email for urgency

• -> If the email is marked ‘Urgent’, send it to the ‘Immediate Attention’ prompt
• -> If not, send it to the ‘Standard Response’ prompt.

Multimodal prompt chaining

This involves prompts that handle different types (or modes) of data, like text, images, or audio, within the same chain. Multimodal just means using multiple formats of information and helps with researching multiple sources.

Here are the pros and cons of multimodal prompt chaining:

Multimodal prompt chaining example:

Take an uploaded product image:

• -> Generate a text description of the product
• -> Translate the description into Hindi (useful for reaching audiences here in Mumbai and across India!).

Dynamic prompt chaining

This is quite flexible. The actual structure of the chain can change while it’s running, based on intermediate results or changing conditions.

Dynamic prompt chaining example:

Start processing a customer request

-> If the analysis shows the request is about ‘billing’

Then, dynamically add a specific ‘Check Payment History’ step into the chain before proceeding.

Recursive prompt chaining

This is useful for very large inputs. You break the input into smaller chunks, apply the same prompt(s) to each chunk individually, and then combine the results.

The example is easy to understand. Hence, I thought I will summarize the pros and cons of recursive prompt chaining:

Recursive prompt chaining example:

Summarize a 500-page book -> Break the book into chapters -> Use a prompt to summarize each chapter -> Combine the chapter summaries into an overall book summary.

Reverse prompt chaining

Instead of starting at the beginning, you start with your desired final output. You work backward to figure out the steps or inputs needed to get there. It’s great for planning or troubleshooting.

Here are the pros and cons of using reverse prompt chaining:

Reverse prompt chaining example:

Goal: Customer buys a product. -> What prompt leads to a buy? (e.g., showing a limited-time offer) -> What leads to showing an offer? (e.g., identifying customer interest) -> What identifies interest? (e.g., analyzing Browse history).

Benefits of using prompt chaining over other prompt engineering techniques

Using prompt chaining isn’t just a different way to talk to AI. It offers some significant benefits. These benefits can make a real difference in the quality and reliability of the results you get.

Here are some key advantages of prompt chaining technique:

Better accuracy and reliability:

Breaking a complex task into smaller steps helps the AI (the LLM) focus its attention. It can concentrate fully on each part. This significantly reduces the chances of errors or misunderstandings that can happen with long, complicated single prompts. Each step builds cleanly on the last, leading to more trustworthy results.

More control over the output:

Chaining gives you much finer control over the AI’s process. You guide the direction at each stage, ensuring the task progresses exactly how you want it to. You’re not just hoping for the best; you’re actively steering the AI.

Handles complex tasks easily:

This is perhaps the biggest win. Tasks that seem too big or complicated for a single prompt become manageable when broken down into a chain. Prompt chaining shines when dealing with multi-step operations or intricate requests.

Clearer and transparent process:

Because you see the output at each step of the chain, the AI’s “thinking” process becomes much clearer. You can follow along and understand how the final result was achieved. This makes the whole process less of a “black box.”

Flexible and modular:

Prompt chains have great modularity.

Think of it like building with LEGO bricks – each prompt in the chain is like a separate block. This modular structure lets you change or update the steps easily. You can also reorder the blocks without having to redo the entire process. This flexibility is fantastic for refining complex workflows.

Easier to fix problems when debugging:

If the final output isn’t quite right, prompt chaining makes it much easier to figure out why. You can examine the output of each individual prompt in the chain. This helps you pinpoint exactly where things went wrong. It saves you time and frustration.

Keeps track of context and information better:

Prompt chaining passes information deliberately from one step to the next. This helps the AI keep context. It also helps the AI remember important details throughout a longer process. This is especially useful to make the most of limited or free versions of AI models by managing context windows.

How to design and implementing prompt chaining in Generative AI?

We understand the “what” and “why” of prompt chaining. Now, let’s get practical and look at how to implement it.

Thinking about how to do prompt chaining involves a few key steps and knowing about some helpful tools.

Ready to build your first prompt chain? Here’s a step-by-step guide to get you started:

Break down your big task

Look at your main goal. What are the smaller, distinct steps needed to achieve it?

For example, if you want to generate a summary email of a long meeting transcript, your steps might be:

• Read the transcript.
• Find the main topics discussed.
• Pinpoint key decisions or action items.
• Draft a summary based on topics and actions.
• Write a concise subject line.

Design a prompt for each step

Now, write a specific instruction (prompt) for each subtask you identified.

Make your prompts clear and focused. You might even develop a standard structure or prompt chaining template for similar tasks to keep things consistent.

Sometimes, you’ll need a special type of prompt called a controlling prompt.

What type of prompt can be used as a controlling prompt?

It’s one specifically designed to dictate the format or focus of the next step’s output. For example, you might end a prompt with: “Summarize the key decisions above in a numbered list.” This controls how the next step should show information.

Structure the prompt chain and pass information

Decide how the prompts connect.

Usually, it’s a simple sequence: Step 1 -> Step 2 -> Step 3. But you could also build:

Conditional prompt chaining:

If the output of Step 2 meets a condition, go to Step 3A; otherwise, go to Step 3B.

Prompt chaining loops

Repeat Step 2 until the output is satisfactory.

The crucial part is passing information between steps. Often, you simply include the output from the earlier prompt in the text of the current prompt (e.g., “Based on the next topics: {output_from_step_2}, find the key decisions.”). For more complex chains, especially when coding, you might use structured data formats.

For example, the documentation for claude prompt chaining suggests using XML tags (like <summary>...</summary> or <action_items>...</action_items>) to clearly label pieces of information passed between prompts. Think of these tags as simple labels helping the AI understand different parts of the input.

Run the chain and handle errors

Execute your prompts in the planned order. It’s also wise to think about basic error handling. What happens if one step fails or gives a weird result? You might need your process to stop, alert you, try the step again, or maybe use a default value.

Helpful prompt chaining tools and prompt chaining frameworks

While you can chain prompts manually (copying and pasting outputs), it gets tedious quickly. Luckily, there are tools and frameworks – which are like programming toolkits – that make this much easier:

LangChain

This is a very popular open-source framework. It provides ready-made components for building LLM applications. It also has specific features for how to chain prompts in LangChain effectively.

IBM has covered this in more detail: Prompt chaining in LangChain

Semantic Kernel

Developed by Microsoft, semantic kernel prompt chaining lets you orchestrate prompts. You can connect to different AI models, and integrate with native code. It’s another powerful choice for building complex chains.

Designing prompt chains with popular AI models

You can apply prompt chaining principles when working directly with AI models too:

Prompt chaining in Claude

As mentioned, claude prompt chaining works well, especially using their suggested structuring techniques (like XML tags).

Here’s the official guide by Anthropic: Chain complex prompts for stronger performance

Prompt chaining in ChatGPT

You can implement ChatGPT prompt chaining by making sequential calls through its API. Application Programming Interface is basically a way for programs to talk to each other. APIs can even manually talk in the chat interface for simpler chains.

Here’s a prompt chaining tutorial to help you get started – OpenAI API tutorial: How to use AI prompt chaining

Here’s another prompt chaining tutorial on using GPT-4o with Flowise AI (a low-code LLM app builder tool):

Prompt chaining in Google Gemini

Google’s Gemini prompt chaining is also possible through its API, letting you code the sequential calls. Learn more: prompt design strategies for Gemini.

Prompt chaining in Microsoft Copilot

Tools like GitHub copilot can incorporate prompt chaining more subtly. This may occur within its code or text suggestions. One suggestion might build upon the context of the earlier interaction.

Microsoft Copilot also has ‘prompt actions’ feature in Copilot Studio: Use Prompt Actions on Copilot

You can also go more advanced and explore dynamic prompt chaining note that this is not exactly prompt chaining. Dynamic chaining is a feature in Copilot Studio that uses AI to orchestrate conversational flows. Here, you let the bot decide which topics and plugin actions to call. It determines their order and how to link them to respond to a user’s inquiry.

It is like Copilot has went a step ahead and helps us design prompt chains for our tasks. Learn more from this dynamic chaining tutorial:

Prompt chaining tutorials on GitHub:

I have found some good resources about prompt chaining on GitHub, explore them to learn more:

Prompt chaining and sequencing tutorial:

Nir Diamant shares techniques for connecting multiple prompts to build logical flows for complex AI-driven tasks. It covers basic prompt chaining, sequential prompting, dynamic prompt generation, and error handling within prompt chains. The implementation demonstrates how to design and execute these techniques effectively.

Learn more: Prompt chaining and sequencing tutorial

The ‘Amazon Bedrock Serverless Prompt Chaining’ repository

This demonstrates how to build complex, serverless, and highly scalable generative AI applications using prompt chaining with Amazon Bedrock. It provides examples of orchestrating workflows through AWS Step Functions and Amazon Bedrock Flows. The examples cover techniques like sequential, parallel, and conditional chains. The repository also includes applications like blog post creation and trip planning. Additionally, AWS Cloud Development Kit (CDK) sample code is provided for implementation.

Learn more: ​Amazon Bedrock Serverless Prompt Chaining

Story writing with prompt chaining

The “Story Writing with Prompt Chaining” notebook from Google’s Gemini API Cookbook demonstrates prompt chaining usage. It helps generate cohesive stories that are contextually rich. It guides users to create a series of interconnected prompts. Each prompt builds upon the earlier one. This helps develop narratives that keep consistency and depth. This technique showcases the Gemini API’s ability to handle complex text generation tasks by effectively managing context across multiple prompts.

Learn more: Story writing with prompt chaining

How to improve prompt chaining output? – 5 prompt optimization strategies

Okay, building a prompt chain is a great start, but how do you make sure it works really well?

Like any process, prompt chains often gain from some tweaking and improvement.

Let’s look at how to improve your prompt chains for the best results.

Use the power of prompt chaining feedback

Feedback is essential for improvement, and it plays a big role in prompt chaining. This happens in a couple of ways:

Built-in feedback:

In a chain, the output of one prompt automatically acts as context or “feedback” for the next prompt. This guides the flow of information naturally.

Adding review steps:

You can also build specific “review” steps into your chain.

For example, one prompt could draft an email. The very next prompt could ask the AI to review that draft for tone and clarity before moving on.

So, what is prompt chaining providing feedback to a previous request?

It’s often about using one prompt’s result to shape the next instruction directly. It might also involve designing prompts specifically to check and refine earlier steps’ work. This cycle is fundamental – providing feedback to a previous request is prompt chaining’s way of self-improvement. Answering what is prompt chaining feedback means understanding this loop of using outputs as inputs or adding explicit review stages.

Improve step-by-step using iterative refining

Optimization rarely happens all at once. It usually involves ‘Iterative Refinement’. This just means making small adjustments, testing the results, and repeating the cycle. For prompt chains, you might:

1. Run the whole chain.
2. Look at the final output. Is it perfect?
3. If not, find which step might be weak.
4. Tweak the wording of the prompt for that specific step.
5. Run the whole chain again and see if the output improved.
6. Repeat until you’re happy with the results.

This step-by-step improvement is much easier than trying to perfect one massive prompt.

Smart prompt design with controlling prompts

Remember those controlling prompts we discussed earlier?

They are vital for optimization.

By clearly telling the AI how to format its response, you make sure of consistency. Specifying what specific aspect to focus on in the next step also ensures quality. Good controlling prompts reduce ambiguity and often lead to better results right away, minimizing the need for later fixes.

Use prompt chaining templates for consistency

Using a prompt chaining template for recurring tasks or steps isn’t just about saving time during setup. It also helps with optimization. When your prompts follow a consistent structure, it’s easier to find areas for improvement. If you find a better way to phrase a particular instruction within your template, you can update the template once. That improvement automatically applies wherever you’ve used it.

Finding and fixing issues using debugging prompt chains

Debugging, which means finding and fixing errors, is much simpler with prompt chains compared to single large prompts. Because you have an output at each stage, if the final result is wrong, you can easily track back through the intermediate outputs:

• Check Step 1’s output: Is it correct?
• Check Step 2’s output: Is it correct based on Step 1’s output?
• Continue until you find the step where things went astray.

Saving or “logging” the output of each step makes this process even easier.

Do you have your own strategy of optimizing prompt chains? Let me know in the comments, I am happy to feature it on this guide!

Challenges of using prompt chaining to consider

Okay, let’s move on to the potential hurdles. While prompt chaining is incredibly useful, it’s good to be aware of some challenges you might face when using it.

A mistake early on can spoil the whole chain

If the AI makes a mistake early in the chain, that error can affect all the next steps. This potentially leads to a flawed final result. This is called as error propagation.

How to avoid error propagation in prompt chaining?

Build in validation steps. Add prompts that check the output of a earlier step for reasonableness or specific criteria before proceeding. You can even include steps that ask the AI to review and correct its own earlier output.

Chains Can Become Complex. Managing intricate chains gets tricky.

Designing, managing, and especially debugging very long or intricate chains (like those with many branches or conditions) can become complicated.

How to manage complex prompt chains?

Use modular design – think of building with reusable blocks (sub-chains) that handle specific parts of the task. Keep clear notes (documentation) about what each step does. Using frameworks like LangChain also helps manage this complexity.

More steps mean more waiting and potentially higher costs

Each step in the chain usually requires a separate call to the AI model. This adds up, potentially increasing the total waiting time (latency) and the cost if you’re using paid AI services.

How to reduce time latency and costs when using prompt chaining?

Improve individual prompts for speed. If your chain branches, run independent branches in parallel (at the same time). Consider using smaller, faster (and often cheaper) AI models for simpler steps in the chain.

You can also use caching. This saves the results of steps so you don’t have to re-run them if the input hasn’t changed.

Debugging can still take effort

When you have too many prompt chains, finding errors in long chains needs extra focus. It is easy to miss issues and asking AI models to debug may or may not be fruitful.

How to reduce debugging effort when using prompt chaining?

Be diligent about logging. Save and examine the input and output for each step in your chain. This makes pinpointing where things went wrong much easier.

Learn more about prompt chaining in Generative AI

Prompt chaining is already making AI more capable, but what does the future hold? Here are 2 key trends that I think one should take note of:

• Tighter integration with AI Agents: Those more autonomous AI Agents we discussed will likely depend more on sophisticated prompt chaining. They will use this internally to plan and execute complex, multi-step tasks reliably.
• More advanced prompt chaining tools: The frameworks and tools designed for prompt chaining will probably become smarter. They will be easier to use. There might be more automation for building, visualizing, and optimizing chains. This evolution benefits developers globally, including those working in tech hubs like Mumbai.

Are you using prompt chaining for your daily interaction with AI models? Hope you are well equipped now to do so!

I have covered more prompt engineering techniques and best practices here:

• What Is Self-Consistency Prompting? – Examples With Prompt Optimization Process – read
• What is tree of thoughts prompting – with examples – read
• Markdown Prompting In AI Prompt Engineering Explained – Examples + Tips – read
• Why Structuring or Formatting Is Crucial In Prompt Engineering? – read
• 20 Prompt Engineering and Generative AI community list across Slack, Discord, Reddit, etc – read
• 16 prompt management tools and adoption best practices – read

I will continue to cover about latest prompt engineering techniques with a strong focus on practical use case and examples. Subscribe to get the latest guides and tutorials:

This blog post is written using resources of Merrative. We are a publishing talent marketplace that helps you create publications and content libraries.

Get in touch if you would like to create a content library like ours. We specialize in the niche of Applied AI, Technology, Machine Learning, or Data Science.