How to Write a Prompt to Analyze and Classify Data in 2025 (Full Guide)

Despite ChatGPT being the second fastest growing application ever and having by February 2025 incredible 400 Million weekly users, I would bet that 99% don’t know how to use it.

What I mean by that is that many people don’t know how to talk to the AI which results in them getting inaccurate results and frustrations.

To avoid all this I’ll show you know how you can write prompts that work consistently with high accuracy.

Here are the topics we will cover in this guide:

1. What is a Prompt?
2. Examples of a Prompt
3. Who This Guide Is For
4. Why Good Prompts Matter
5. How a Bad Prompt Looks Like
6. Prompt Breakdown and Implementation Steps

But What is a Prompt Anyway?

Simple answer: A prompt is the input you give to an AI to get a specific output.

Don't be confused if you have read other articles that wrote lengthy paragraphs about "what is a prompt," because in reality, a prompt is not complicated to define.

What's tricky is writing a good prompt, but before I show you how to write one, let me give you 3 examples of a prompt.

3 Examples of a Prompt

One Command Prompt

In its simplest form, a prompt can be a command like:

“Create me an essay about the Renaissance"

Question Prompt

A prompt can be a simple question like:

"Are roses red?"

Tagged Prompt

A prompt can also be a more detailed set of instructions that can edit, analyze, or clean your data to personalize cold emails, analyze customer feedback, clean company names, and more.

I call this a tagged prompt because it has a tag for each section — see example below — to explain to the AI what each section is about; otherwise, the AI will make its own conclusions about the sections.

But wait before you go and say that's not practicable after seeing this prompt.

I'll break it down.

Who This Guide Is For

Writing effective prompts is a challenge that everyone will face at some point in their career.

That's why this guide isn't designed for a specific industry or role but rather for those working with data and automation — we'll specifically focus on writing prompts that help you edit, analyze, and clean large datasets efficiently, whether you're a student or experienced professional.

All tests for this and our other prompts were run with Datablist.com.

Why Good Prompts Matter

Think of it as instructing an assistant that has zero agency but would do everything right if you just tell it what to do—the clearer and more specific your request, the better the task will be fulfilled.

How a Bad Prompt Looks Like

Why This Prompt is Bad:

• No structured instructions: The requirements are scattered throughout the text without clear organization.
• No clear sections or separators: The AI has to parse through a wall of text without any logical breaks.
• Placeholders mixed in text: Placing {{company_about}} and {{Website Texts}} randomly in the middle makes it harder for the AI to properly replace these values.
• Informal language: Using phrases like "stuff" and "btw" creates ambiguity.

How the AI Sees the Prompt

When this prompt is actually used with data, for the AI it looks like this:

When these placeholders get replaced with actual data, the result is a jumbled mess that makes it very hard for the AI to understand and process properly.

Here's what goes wrong in this case:

1. Confusion & placeholder issues: When placeholders get replaced, the AI will fail to properly separate the two different data sources.
2. Interpretation challenges: The AI will struggle to understand the exact criteria for HR companies.

👉 With that being said: let's now break down how a good prompt looks like and how to create one!

Prompt Breakdown and Implementation Steps

A Breakdown of Our Prompt Structure (The Good Prompts)

When you look at some of our AI prompts templates in the Datablist.com AI-prompts library, you'll notice that almost every prompt template is built out of sections and each section has these 3 components:

1. Tag: Lets the AI know what the section is about
2. Body: The body contains instructions that help the AI understand and complete the task through:
   • Explanation: Tells the AI something it should know/Gives the AI context
   • Command: Tells the AI what to do/Gives the AI a task
3. Separators (===): We use separators for 2 things:
   • Visual separation: The separator creates clear visual breaks between sections, making the prompt easier to read, understand, and edit for humans.
   • AI parsing: The separator helps the AI identify and process sections of the prompt more effectively, leading to better responses.

At the end we also have: Placeholder properties:

• Columns that get replaced with data when the prompt is used.
• These placeholders are used to customize the prompt for each record in a dataset.

For example, in our Job Title Categorizer prompt we use {{job_title}} as a placeholder that gets replaced with an actual job title from our dataset when processing each record.

The Breakdown of My Prompt & Implementations Steps

Let see again our prompt example for earlier.

Note: HR Companies aren't our ICP ("Ideal Customer Profile") — we just used it as an example.

First Step: Context

When providing context to the AI, I did three key things:

• Provided information about what data the AI would receive, without revealing the specific targeting criteria
  • This keeps the AI unbiased in its analysis
• Explained the context around the data and outlined the specific problem
  • This helps the AI understand what it's working with
• Described my specific situation
  • This allows the AI to tailor its approach to my needs

What you have to do when writing your prompt:

• Tell the AI about the data you're going to give it
• Describe your problem
• Use separators (===)

Second Step: Task/Command (What I Want You To Do)

In this section of the prompt, I did two crucial things:

• Framed the AI again with the tag "What I want you to do" so it knows what I am going to talk about
• I gave it just the broad description of the task without mentioning the steps to take or mentioning which data to look for, to keep the information flow logical

What you have to do when writing your prompt:

• First, identify the core purpose of your task/command.
• Second, use separators (===)

Here's an example:

Core tasks:

• Cleaning and structuring data and flagging data quality issues
• Creating a scoring model to prioritize companies

If you have similar tasks to fulfill, don't try to do them all with one prompt as it would be very difficult for the AI to handle and for you to detect potential errors.

Third Step: Command Instructions (How To Do It)

These are the things I do when adding instructions to a prompt:

• Keeping it simple and straightforward
• Explaining the task as if I would teaching someone to do it manually
• Focusing on clear, step-by-step instructions

Remember that AI excels at repetitive tasks when given proper instructions. If you're not getting the desired results, it's usually because your instructions need to be clearer or more specific - not because the AI is doesn’t know how to do it.

What you have to do when writing your prompt:

• Create a simple step-by-step instruction using simple and clear language
• Use separators (===)

Here's an example of applying command instructions to the client's M&A data analysis request:

Fourth Step: Mistake Prevention no.1 (Context About the Task) – Optional

This step gives additional task-specific context to help the AI:

• Understand nuances of the task
• Avoid common pitfalls
• Make better decisions

By being explicit about potential issues and edge cases, we create guardrails that prevent the AI from making assumptions or mistakes.

This step isn’t required in every prompt but it is crucial for complex tasks.

What you have to do when writing your prompt:

• Think of common mistakes that junior employees make when doing this type of task
• Include these potential mistakes in your prompt to help the AI avoid them
• Use separators (===)

Example for M&A client data analysis prompt:

Fifth Step: Goal (What You’re Looking For)

These are the most important things to include when writing your expectations in a prompt:

• Providing detailed context about the desired outcome
• Being specific about expected results
• Avoiding revealing biases or desired conclusions that could influence the analysis

Using natural, conversational language instead of technical jargon typically leads to better results from AI systems.

The fifth part of our prompt breakdown: Describing what we are looking for

What you have to do when writing your prompt:

• Define clear outcomes you want to achieve from the analysis or task
• Be specific about what indicators or signals matter most to you
• Give it examples
• Use separators (===)

Example for M&A client data analysis prompt:

We simplified this example to keep the article concise - the actual data structure details are excluded but would typically include specific metrics and calculations for each criterion.

Sixth Step: Data Context (Mention About the Data) – Optional

When I add data context to my prompts, I follow these rules:

• Reinforce mistake prevention through clear rules and gave it a reason
  • Rule example: "Always evaluate complete context"
  • Reason: "Single indicators can be misleading"
• Add multiple layers of context
  • Potential mistakes to avoid
  • Data-specific issues
  • Task-specific reminders

The sixth part of our prompt breakdown: Mistake Prevention no.2

What you have to do when writing your prompt:

• Think of potential mistakes the AI could make and explicitly instruct it to avoid those specific errors.
• Split information into logical parts instead of providing it all at once.
• Use separators (===)

Example for M&A client data analysis prompt:

By being explicit rather than letting the AI make assumptions about the prompt, you will prevent many mistakes

Seventh Part: How to Start (First Step)

This final step involves providing clear starting instructions to the AI. Here's what I did:

• Kept it simple and straightforward
  • Numbered the steps chronologically
  • Used clear action words like “read, make, decide…”

The seventh part of our prompt breakdown: Telling the AI how to start

What you have to do when writing your prompt:

• Give clear starting instructions with numbered steps
• Use action verbs to make the steps easy to follow
• Keep instructions simple and straightforward
• Use separators (===)

Example for M&A client data analysis prompt:

Eighth Step: Placeholder Format (Always Last)

This is the final step in writing an effective prompt: insert columns as a placeholder.

This step must come last because placing data placeholders earlier in your prompt can lead to incorrect results.

What you have to do:

Use double curly braces {{ }} to add data from your spreadsheet columns

Use separators (===)

All placeholders should be inserted at the end of the prompt to ensure correct results.

Conclusion:

Writing good prompts is a must-have skill in today’s work environment whether you use ChatGPT, Claude, DeepSeek aor any other AI model, knowing how to write a good prompt is the only way to more accurate, relevant, and useful responses. By following the principles outlined in this guide, you'll be able to use AI effectively and maximize your productivity.

Frequently Asked Questions About Writing AI Prompts

What is a good AI prompt?

A good AI prompt is clear, specific, and well-structured. It includes context about what you want to achieve, specific instructions on how to do it, and the desired format for the output. The prompt should be written in simple language and avoid any ambiguity that could lead to misunderstandings.

Check our AI prompts examples for lead generation.

Why are my ChatGPT prompts not working?

AI prompts might not work effectively because of:

• Unclear or vague instructions
• Too many tasks in a single prompt
• Missing context or background information
• Complex or technical language
• Lack of specific examples or desired output format

How do I write AI prompts for better results?

To write effective AI prompts:

• Start with a clear objective
• Break complex tasks into smaller steps
• Provide relevant context and background
• Specify the desired output format
• Use simple, straightforward language
• Include examples when possible

What are the most common mistakes in prompt writing?

Common prompt writing mistakes include:

• Being too vague or general
• Overloading the prompt with multiple tasks
• Not providing enough context
• Using unclear or ambiguous language
• Assuming the AI understands implied information
• Not specifying the desired output format

How long should an AI prompt be?

An AI prompt should be as long as necessary to convey all essential information, but not longer. Typically, effective prompts range from a few sentences to a paragraph for simple tasks, and may be longer for complex tasks requiring detailed instructions and context. The key is to be comprehensive yet concise.

What is prompt engineering?

Prompt engineering is the practice of designing and optimizing inputs to AI models to get the most accurate and useful outputs. It involves understanding how AI models interpret instructions, structuring prompts effectively, and iteratively improving prompts based on results.