Introduction

If you’ve scrolled on LinkedIn recently, you’ve probably seen everyone bragging about how AI has made them 10X more efficient. But when you tried using ChatGPT, or Google Gemini, or whatever LLM you prefer, did you feel like something was… missing? Like the outputs were just not as polished, accurate, or relevant as you expected? 

That’s exactly what this module is designed to fix. We’ve got tons of practical advice on how you can immediately improve your prompts. And unlike generic AI prompting courses, this module is specifically tailored for Customer Success professionals. Every example, every tweak, and every strategy is meant for the work you actually do. 

Some of the things we'll cover include:

• Examples of prompts fine-tuned for Customer Success Managers
• Optimizing prompts for customer data analysis 
• The fundamentals of using agents for Customer Success

The best part is, despite how much of an impact these tips can have on the quality of your outputs, the actual tweaks to your prompts aren’t too complex. With some small (but smart) changes in the right places, you’ll have the AI delivering exactly what you need for your CS function. So by the end of this module, you’ll be able to:

• Write and refine clear, actionable prompts that get AI to deliver exactly what you need for CS tasks.
• Apply advanced prompting techniques, such as prompt chaining and tree of thought, for more complex tasks.
• Experiment with AI agents to simulate scenarios and get expert feedback on meetings, flows, or customer interactions.

Let's find out how great prompts can make AI your most powerful tool for Customer Success!

Chapter 1: The 5-Part Framework to Effective Prompting

Emails that need a dozen rewrites, reports that skip the details you actually care about, or insights that are just vague. If this sounds like the kind of outputs you get when you ask AI for help, it’s because your prompts are lacking some core components. The simplest fix for this is to follow the 5-part framework outlined in this chapter. 

This framework is based on the official prompting framework developed by Google:

T - Task

C - Context

R - Reference

E - Evaluate

I - Iterate

You can use this mnemonic to remember it: Thoughtfully Create Really Excellent Inputs. But if you find this hard to remember, use this version we developed that might be easier for a CS professional to recall: Today's Customers Really Expect Insights. 

T - Task

Most people go straight into writing a prompt without a clear vision of what they want the AI to do. This usually results in the AI guessing, and you ending up with a vague or generic output.

Take a moment to define the task. Be explicit about what you want the AI to produce. Are you asking for an email draft, a product recommendation, or an analysis of customer data? What length are you expecting the response to be? Do you want the format of the response to be in bullets, tables, or paragraphs?

The clearer you are about the task, the better the AI can understand and provide a response you like. 

Example:

Write a 200-word email to a customer explaining a delayed rollout of a new feature in their subscription plan, and outline the steps your team is taking to resolve it in bullets afterwards.

Pro Tip: Use action verbs, like “summarize”, “list”, or “outline”, to describe the required action. You’ll get a response more specific to the task you have in mind. 

C - Context

If you’re clear about the task, you’ll get a pretty relevant response. But it won’t be polished as it would be if you gave the AI context. Without context, responses will usually be technically correct but missing nuance, lacking relevance, or failing to emphasize what matters most to the customer. 

Try adding relevant background information so that the AI has all the details it needs to make informed decisions. Context can look like customer history, product details, tone preferences, or any other relevant facts that help narrow down the scope of the AI’s response.

Example:
The customer is on a Premium subscription plan and was scheduled to receive Feature X last week. The rollout was delayed due to technical updates. The tone should be professional yet empathetic and reassuring. 

Pro Tip: Use personas to guide the AI. For example, starting a prompt with “You are an Operations Analyst” gives the AI context for tone, style, and focus. 

The richer the context, the more tailored and actionable the AI's output will be.

R - References

We often assume the AI is already familiar with the right style, tone, or company standards that is required in our work. But that means the AI might produce content that’s inconsistent with your brand voice, misses key phrasing conventions, or doesn’t follow your internal processes. 

A fantastic way to guide the AI in the right direction is providing references. 

These could be articles, documents, or specific guidelines. This step is especially helpful when you want the AI to align with your company's tone, style, or specific industry knowledge.

Example:
Provide the AI with a link to the company’s Premium subscription rollout email template or internal communication guidelines for feature delays.

E - Evaluate

Once you've written your prompt, take a moment to evaluate it. A common mistake is always waiting until after generating the output to realize there were gaps in the prompt, which forces rework and wastes time. 

The goal here is to make sure that your initial prompts are good enough that the AI has everything it needs to provide useful outputs from the get go. If it doesn’t have a solid initial prompt as a base, you’re going to be stuck in a long back-and-forth with the AI as you gradually try to adjust the prompt each time it gives an inadequate response. 

To evaluate, there are a few questions you can ask yourself. Does the content of the prompt actually address the intended task? Does the prompt include enough details to guide its response? Is the prompt clear enough that the AI won’t misinterpret it? 

Example:
Evaluate the prompt to check if it requires the AI to highlight the key steps your team is taking to resolve a delayed feature rollout, reflect the account’s Premium subscription status, and maintain a professional yet empathetic tone.

I - Iterate

AI isn’t perfect, which means that even after you get good at evaluating your prompts and refining them, the first response you get might not fully meet your expectations. So the final step you can take is iteration. 

This is where you can adjust your prompt based on the output you receive. If the AI missed a key detail or didn’t quite capture the tone you wanted, you can rephrase your prompt or provide more context. Think of this as an ongoing conversation with the AI, where you improve the quality of the results with each iteration.

Example:
Rewrite the email keeping the content professional and informative, but make the tone friendlier and more conversational. Focus more on highlighting the steps our team is taking and the reassurance about next steps.

Now that you’ve learned the foundation of effective prompting with the 5-part framework, it’s time to go more in-depth. In the next chapter, let's take the iteration process we mentioned, and look at some different practical strategies to refine and tweak your prompts. 

Chapter 2: Iteration Methods

Emails that need a dozen rewrites, reports that skip the details you actually care about, or insights that are just vague. If this sounds like the kind of outputs you get when you ask AI for help, it’s because your prompts are lacking some core components. The simplest fix for this is to follow the 5-part framework outlined in this chapter. 

This framework is based on the official prompting framework developed by Google:

T - Task

C - Context

R - Reference

E - Evaluate

I - Iterate

You can use this mnemonic to remember it: Thoughtfully Create Really Excellent Inputs. But if you find this hard to remember, use this version we developed that might be easier for a CS professional to recall: Today's Customers Really Expect Insights. 

T - Task

Most people go straight into writing a prompt without a clear vision of what they want the AI to do. This usually results in the AI guessing, and you ending up with a vague or generic output.

Take a moment to define the task. Be explicit about what you want the AI to produce. Are you asking for an email draft, a product recommendation, or an analysis of customer data? What length are you expecting the response to be? Do you want the format of the response to be in bullets, tables, or paragraphs?

The clearer you are about the task, the better the AI can understand and provide a response you like. 

Example:

Write a 200-word email to a customer explaining a delayed rollout of a new feature in their subscription plan, and outline the steps your team is taking to resolve it in bullets afterwards.

Pro Tip: Use action verbs, like “summarize”, “list”, or “outline”, to describe the required action. You’ll get a response more specific to the task you have in mind. 

C - Context

If you’re clear about the task, you’ll get a pretty relevant response. But it won’t be polished as it would be if you gave the AI context. Without context, responses will usually be technically correct but missing nuance, lacking relevance, or failing to emphasize what matters most to the customer. 

Try adding relevant background information so that the AI has all the details it needs to make informed decisions. Context can look like customer history, product details, tone preferences, or any other relevant facts that help narrow down the scope of the AI’s response.

Example:
The customer is on a Premium subscription plan and was scheduled to receive Feature X last week. The rollout was delayed due to technical updates. The tone should be professional yet empathetic and reassuring. 

Pro Tip: Use personas to guide the AI. For example, starting a prompt with “You are an Operations Analyst” gives the AI context for tone, style, and focus. 

The richer the context, the more tailored and actionable the AI's output will be.

R - References

We often assume the AI is already familiar with the right style, tone, or company standards that is required in our work. But that means the AI might produce content that’s inconsistent with your brand voice, misses key phrasing conventions, or doesn’t follow your internal processes. 

A fantastic way to guide the AI in the right direction is providing references. 

These could be articles, documents, or specific guidelines. This step is especially helpful when you want the AI to align with your company's tone, style, or specific industry knowledge.

Example:
Provide the AI with a link to the company’s Premium subscription rollout email template or internal communication guidelines for feature delays.

E - Evaluate

Once you've written your prompt, take a moment to evaluate it. A common mistake is always waiting until after generating the output to realize there were gaps in the prompt, which forces rework and wastes time. 

The goal here is to make sure that your initial prompts are good enough that the AI has everything it needs to provide useful outputs from the get go. If it doesn’t have a solid initial prompt as a base, you’re going to be stuck in a long back-and-forth with the AI as you gradually try to adjust the prompt each time it gives an inadequate response. 

To evaluate, there are a few questions you can ask yourself. Does the content of the prompt actually address the intended task? Does the prompt include enough details to guide its response? Is the prompt clear enough that the AI won’t misinterpret it? 

Example:
Evaluate the prompt to check if it requires the AI to highlight the key steps your team is taking to resolve a delayed feature rollout, reflect the account’s Premium subscription status, and maintain a professional yet empathetic tone.

I - Iterate

AI isn’t perfect, which means that even after you get good at evaluating your prompts and refining them, the first response you get might not fully meet your expectations. So the final step you can take is iteration. 

This is where you can adjust your prompt based on the output you receive. If the AI missed a key detail or didn’t quite capture the tone you wanted, you can rephrase your prompt or provide more context. Think of this as an ongoing conversation with the AI, where you improve the quality of the results with each iteration.

Example:
Rewrite the email keeping the content professional and informative, but make the tone friendlier and more conversational. Focus more on highlighting the steps our team is taking and the reassurance about next steps.

Now that you’ve learned the foundation of effective prompting with the 5-part framework, it’s time to go more in-depth. In the next chapter, let's take the iteration process we mentioned, and look at some different practical strategies to refine and tweak your prompts.