How Chain of Thought Prompting Improves Your ChatGPT Outputs

Google is cracking down on scaled content abuse. But that doesn’t mean you have to stop scaling your content engine. It doesn’t even mean you have to stop using AI to support scaling. It just means you can’t rely on spammy, thin content to compete.

So, how do you embrace AI to increase your content volume without being penalized?

It’s simple: embrace AI as an augmentation tool — not a replacement for human creators.

One way to augment your content creation efforts is using new AI tactics like chain of thought prompting to create higher-quality content faster. 

Let’s take a deep dive into the world of CoT and how it can help you improve your content engine. 

What is Chain of Thought Prompting? 

Chain of thought prompting (CoT) is a technique used to improve the reasoning abilities of large language models (LLMs). The core idea is to encourage the model to break down a complex problem into smaller, more manageable steps, providing an explicit chain of reasoning that leads to the final answer. 

It’s similar to the way you learned how to solve math or reasoning problems back in school. Here’s a quick (highly simplified) version of how it works: 

• Problem Transformation: Instead of simply presenting a task or question to the LLM, a chain of thought prompt restructures the input to include the phrase, “Let’s think step by step.” This cue signals the model to generate intermediate reasoning steps.
• Intermediate Reasoning:  The model is encouraged to explicitly lay out its thought process, step by step, leading toward the solution. This might involve calculations, logical deductions, or recalling relevant information.
• Final Answer: The model arrives at the final answer, ideally supported by the transparent reasoning chain it produced.

This type of prompt can be delivered in a single prompt, as is shown in the example below, or you can break up the steps across a number of sequential prompts that build off one another (more on this later).

The approach was first coined by Jason Wei and a team of experts from the Google Research Brain Team in a paper in 2022. The example below is from the paper that introduced CoT:

• The standard prompt contains an example problem and solution, as well as the question that the AI needs to answer. 
• The CoT prompt includes the same problem-solution set, a series of steps outlining how that solution was reached, and then a new question for the AI to answer. 

As you can see, the model fails to answer the first problem correctly but succeeds when prompted to walk through a “chain of thought” to get there. 

This paper identified some key benefits that this prompt engineering tactic brings to a number of different reasoning problems:

• The step-by-step process forces the AI to think more thoroughly and avoid making jumps in logic, which helps improve results.
• CoT provides a window into the AI’s decision-making process, giving users and model designers more insight into how these models work. 
• By requiring an explanation as to why the AI chooses a specific output, CoT can potentially mitigate some of the biases that LLMs are prone to.
• AI users can improve outputs simply by including statements like “let’s take it step-by-step” or “explain your reasoning,” similar to the emotion prompts tactic.

Most importantly, this tactic has the potential to improve the result of any language-based task you feed to ChatGPT, Gemini, Jasper, or other LLM-based AIs. In fact, Google has been training its models with CoT since 2022:

Pathways Language Model (PaLM) is a new advanced AI model that uses a technique called chain of thought prompting to do complex tasks like solve math word problems — and even explain its reasoning process step-by-step. #GoogleIO pic.twitter.com/YHLsnqNemv

— Google (@Google) May 13, 2022

Okay, now that you know a bit more about chain of thought prompting, let’s get into the ways you can use it to improve the efficiency of your content creation. 

How Chain of Thought Prompting Helps with Content Creation

Using chain of thought prompting to improve outputs for writing tasks is more complex than simple math or commonsense reasoning. There are specific rules for math and word problems that lead you to a single correct answer, but there are many different ways to approach strategy or content creation. 

There’s an art to SEO copywriting, content marketing, and distribution. That said, there’s also a lot of reasoning involved in these tasks, especially at enterprise scale.   

The original CoT study from Google emphasizes that this prompting style significantly enhances the reasoning abilities of LLMs by providing a step-by-step, structured approach that mimics human problem-solving processes. 

The rationale for its applicability to writing tasks lies in the nature of writing as a process that requires multi-step reasoning, planning, organization of thoughts, and the development of ideas in a coherent and logical manner. 

Writing tasks, especially those that involve creating narratives, argumentative essays, or technical reports, can benefit from a CoT approach that outlines the thought process leading to the development of the final piece.

Here’s how the underlying principles of chain of thought prompting — structuring thought processes, breaking down complex problems, and guiding logical progression — align well with the requirements of effective writing. 

1) Topic Research

The B2B SaaS space is full of highly technical niches — from cloud infrastructure to feature management to, well, AI. 

Whether you work as a freelancer, at an agency, or even for a large brand with lots of API integrations, quickly digesting new topics is likely central to the gig. 

Chain of thought prompting can be beneficial for this task. When trying to understand a new topic, breaking down the learning process into a series of questions and answers, each building on the last, can help gradually build a comprehensive understanding of that topic. 

CoT can guide the model to sequentially explore different facets of the topic, making complex information easier to digest and understand.

Let’s say you work at an agency and just got assigned to a cloud infrastructure product like Hashicorp’s Terraform. To give you an example of the complexity you’re dealing with, here’s an explanation of the product from Wikipedia:

Infrastructure-as-code (IaC). Declarative configuration language. JSON. Right off the bat, those are three highly complex topics. 

Let’s see if I can get ChatGPT to speed up the learning process by identifying the most relevant concepts related to those three keywords. 

By tacking on “explain the rationale behind the concepts you select,” I’m (theoretically) prompting ChatGPT in a way that directs it to provide a more deliberate answer. 

As you can see below, each of the key concepts for IaC is accompanied by an explanation of its importance.

Through this approach, you learn that it’s not just important to know about tools like Terraform and Ansible; it’s also important to know that the former is provider-agnostic and the latter provides agentless configuration management.

This is the type of context that can help you break down and digest complex context more quickly — very useful for a cloud infrastructure novice like me.  

Now let’s look at the direction you’d get if you just gave a normal prompt without directing the AI to explain its “thought process:”

The information contained under each numbered bullet here is important, for sure, but they don’t come with the same level of context. 

So, by simply adding a statement like “explain your reasoning” or “walk through this step-by-step,” you can boost the value you get from each interaction with your AI. 

2) Brief and Draft Generation 

As someone who primarily focuses on creating long-form written content, I’m well-versed in the intricacies (and madness) of the creative process. The journey from an idea to a finished piece passes through the same checkpoints — topic planning to research to briefing to drafting — but the path between those points is more meandering for some than others. 

One of the biggest benefits I’ve gotten from generative AI so far is streamlining aspects of the creative workflow. AI is helpful for getting the ball rolling on creative briefs and rough drafts, and CoT prompting has the potential to help even more. 

The process of generating a creative brief or drafting a document often involves organizing thoughts, outlining key points, and developing a narrative or argument structure. 

CoT prompting can help by structuring the thought process that leads to the final document, ensuring that the draft is coherent, logically organized, and aligns with the intended message or objective.

For example, let’s say I need to create a top-of-funnel blog post explaining what a Human Resource Information System (HRIS) is. The marketing strategy team has given me a working title, primary keyword, and list of secondary keywords to target throughout the piece as well.

I can feed the generative AI this information and ask it to create a creative brief outline: 

In this instance, I included an example of the completed subheaders for the Definition and Purpose sections, as well as the Evolution of HRIS section, to give the AI an example of how it should proceed. It’s similar to the original example from the Google study, where a math problem and answer set are included in the prompt (although at a more complex level).

Here’s a part of what the AI gave back:

The formatting changed slightly from my example in the prompt, but the output itself is still useful. Each header and subheader includes “instructions” on how to create the draft in full. 

These creative brief sections provide a great starting point for an SEO-driven piece, and it took all of five minutes for me to set up the initial prompt. Now, I can go through the brief and do the following: 

• Scan for any hallucinations or inaccuracies
• Add in the internal and external links to be included during drafting
• Tweak the tone/voice to better match the brand
• Add additional directions such as where to place images or CTAs

I can even take things a step further and turn this creative brief into a rough draft using the context from the interaction above.  Here’s the prompt I used to do it: 

 In a matter of minutes, ChatGPT turned the creative brief above into nearly 1,000 words, serving as a great starting point for the drafting stage. You can check out the full interaction here. 

Again, this output is not ready for publishing as is. There’s lots of fact-checking, rewriting, and optimization that needs to be done before I would even submit it to an editorial team. 

But still, this type of step-by-step prompting can be a major help if you’re struggling with things like writer’s block (or just want to shave an hour or two off the creation time).

By breaking down the writing task into a series of intermediate steps or thoughts, similar to the chain of thought prompting used in reasoning tasks, you can guide the model to produce more coherent and logically structured outputs. 

3) Incorporating Feedback into Documents 

Another great application for chain of thought prompting is during the revision phase. 

As most writers, copywriters, and other creatives know, you rarely get things right on the first (or even second) try. This is especially true when you’re creating content for your company or a client — getting the voice, message, and structure right is a team effort. 

To potentially reduce the scope of revisions, you can use AI as a sounding board. 

CoT prompting can be beneficial for this task as well. Incorporating feedback involves understanding the critique, identifying areas for improvement, and systematically addressing each point. 

CoT can structure this process by breaking down the feedback into actionable steps, facilitating a more organized and effective rewrite. It can guide the model (or user) through the feedback, ensuring that they address all points comprehensively.

For instance, let’s say you got some high-level feedback about the structure of a creative brief. You’ve missed the mark by focusing on “X” instead of “Y” and need to rework some sections. You can feed all this information to tools like ChatGPT to help speed up these revisions by following these steps: 

1) Feed the AI the copy from your creative brief and ask it to provide a summary of the brief’s contents. 

2) Collect all the feedback you received on the document and feed that to the AI as well, asking it to provide a summary of the mistakes/missteps.

3) Prompt the AI to create a new creative brief based on the context provided in the original brief and feedback.

4) Take the new creative brief and refine it with your own insights and input from your creative team. 

You can also use CoT prompts for more granular feedback requests about style, voice, and tone. 

Say you’ve just undergone a rebrand or are working for a new client and need to quickly weave an entirely new brand voice into your content. You can feed the AI some examples of on-brand content from other blog posts, web pages, or social posts and direct it to rewrite your content in a way that aligns with the brand voice.

Here’s an example — I take a bland paragraph about the importance of content distribution and get the AI to rewrite it in Ross’s voice and style:

In this case, it not only rewrites the “Plain Text” section but elaborates on it using Ross’s voice and style. There are a few poorly worded AI-isms, such as starting with “In today’s digital marketing realm,” but other than that, it’s a pretty good approximation. 

By providing ChatGPT with examples of the desired voice, or even feedback that you’ve received about achieving said voice, you can quickly align your text with brand guidelines. 

4) Identifying Distribution Candidates

Another way chain of thought prompting can assist with your content creation workflow is by helping identify the best segments of an asset for content distribution. 

You need to rely on more than search engine algorithms or paid ads to get your blog post, landing page, or gated asset in front of your audience. 

There are lots of ways you can approach content distribution, but the strategy behind it is always the same: give readers key snippets that will hook them in and get them interested in the full piece. 

Typically, these snippets fall under the 4 Es of Content Framework — educate, entertain, engage, or empower. But it can be difficult to predict which sections of your content will have the biggest impact on your audience. 

You can use chain of thought prompting to extract the most compelling parts of a piece to quickly generate a list of potential distribution content. 

Let’s look at an example using a recent post on Foundation Labs blog about growth hacking strategies. 

I copied the text from the blog post and pasted it below the prompt so the AI could scan it for sections I could use to distribute the post on LinkedIn. 

Here’s what it came up with:

The six examples provided by the AI cover a number of topics discussed in the piece that would be of interest to SaaS leaders and digital marketers: 

• The philosophy of experimentation and unconventional tactics that underlie growth hacking 
• The popularity of r/growthhacking on Reddit as a benchmark for sustained interest in the practice and a forum for finding examples 
• Examples of how SaaS brands and others have hacked growth using freemium offerings, social comments, embedded Looms, and SEO heists

The best part is that by tacking on the statement “explain your rationale behind each selection” in the prompt, the AI uses a chain of thought approach to solve the task. This comes in handy when it’s time to turn each topic into actual distro post copy — the model has already identified the key points that are worth highlighting and why. 

Now, let’s turn each of these topics into a distro post. In the prompt below, I include an example of a recent LI post from Ross, turning this into a CoT prompt that should improve its output. 

As you can see below, each distro post includes a catchy tagline, roughly 150 words of copy, and even some hashtags. That’s not a bad start at all. 

Of course, these are still far from being finished products. There are AI-isms that need to be removed (“In the digital age,” “In the SaaS world,” “It’s not just about X, it’s about Y,” etc.), and the formatting can be adjusted for easier readability.

With your handy AI assistant, a few distro post examples, and about 10 minutes, you can extract half a dozen distribution examples from any piece of content.

Now, you can save some time on the manual steps in your distro content creation process and focus more on refining these posts from a strategic and brand perspective. 

Learn How to Improve Your Content Workflow with AI

The strategy behind chain of thought prompting — breaking down complex tasks into manageable steps — is helpful for more than just mathematical and common sense reasoning tasks.  Creatives, marketers, and strategists can all apply this step-by-step rational approach to almost every interaction with AI tools.

But, like all interactions with generative AI, there’s an element of randomness in how these tools process requests and what results they’ll spit out.

That makes it all the more important to experiment with different prompts to increase your understanding of these powerful models.

If you want to kickstart your learning process with over 130 prompts tailored for marketers and content creators, check out our AI Marketing Console.