Sitemap

You’re Prompting ChatGPT Wrong: The 7-Component Framework That Actually Works

6 min readJun 14, 2025

--

Why your AI results are mediocre and how to fix them with structured prompting

I watched a marketing director spend forty-five minutes fighting with ChatGPT last week. She was trying to get it to write product descriptions for her e-commerce site, but every output was either too generic, too long, or completely off-brand. After a dozen iterations, she threw her hands up and said, “AI just doesn’t work for creative tasks.”

But the problem wasn’t the AI. It was her approach.

She was treating ChatGPT like a magic box where you throw in random requests and hope for the best. What she needed was a systematic approach to prompting that would give her consistent, high-quality results every time.

That’s exactly what I’m going to teach you today.

Why Most People Fail at Prompting

The biggest mistake people make with AI is thinking that natural language means casual language. Just because you can talk to ChatGPT like a human doesn’t mean you should approach it the same way you’d ask a colleague for help.

When you ask a colleague to “write something about our new product,” they bring context you don’t have to explain. They know your company, your audience, your brand voice, and your goals. They can read between the lines and fill in the gaps.

AI doesn’t have that context. It’s incredibly powerful, but it needs explicit guidance to deliver what you actually want.

“The difference between good and great AI output often comes down to how well you structure your input,” one AI researcher told me recently. “The models are capable of amazing things, but only if you give them the right framework to work within.”

This is why I developed a seven-component framework that transforms vague requests into precise instructions that consistently deliver professional-quality results.

Component 1: Role and Objective

The first component sets the stage by telling the AI exactly who it should be and what its primary goal is. This isn’t just about getting better outputs, it’s about activating the right knowledge and reasoning patterns within the model.

Instead of starting with “Write a blog post about marketing,” try “You are an expert content strategist with 10 years of experience in B2B SaaS marketing, tasked with creating educational content that drives qualified leads.”

The specificity matters because it helps the AI understand what knowledge to draw from and what success looks like. When you define a clear role, you’re essentially telling the AI which version of itself to be.

Don’t be vague here. “Help me with writing” is far less effective than “You are a technical writer specializing in API documentation, focused on helping developers quickly understand and implement new features.”

Component 2: Instructions and Response Rules

This is where most people’s prompts fall apart. They give the AI a general direction but no specific constraints or guidelines. The result is output that’s technically correct but practically useless.

Effective instructions are specific, unambiguous, and include clear boundaries. Use bullet points for multiple requirements, and always define what NOT to do.

For example: “Summarize the following research paper. The summary must be exactly three sentences long. Use language accessible to high school students. Do not include personal opinions, interpretations, or information not explicitly stated in the original text.”

The power is in the constraints. When you tell the AI exactly what you want and what you don’t want, you eliminate the guesswork that leads to disappointing results.

Component 3: Context

Context is everything in AI prompting, but it’s the component most people skip. They assume the AI will understand the background, but that’s not how these systems work.

Give the AI all the relevant information it needs to make good decisions. This includes background information, relevant data, constraints, and any other details that would help a human understand the task.

If you’re asking for help with a customer email, don’t just say “help me respond to this complaint.” Include the full customer email, your company’s return policy, the customer’s purchase history if relevant, and your typical response style.

“I see people getting frustrated with AI because they’re asking it to read their minds,” a product manager who works extensively with AI tools explained to me. “The models are incredibly capable, but they’re not psychic.”

Component 4: Examples (Few-Shot Prompting)

Examples are one of the most powerful tools in your prompting toolkit, but they’re criminally underused. When you show the AI exactly what good output looks like, you dramatically improve your chances of getting similar quality results.

This is called few-shot prompting, and it works because it demonstrates format, style, tone, and level of detail more effectively than any description could.

If you want ChatGPT to write product descriptions, don’t just describe what you want. Show it 2–3 examples of great product descriptions and explain why they work. Include the input (product details) and the desired output (final description) for each example.

The AI learns from patterns, and examples provide the clearest pattern possible.

Component 5: Reasoning Steps (Chain-of-Thought)

For complex tasks, asking the AI to “think step by step” can dramatically improve output quality. This technique, called chain-of-thought prompting, forces the AI to break down complex problems into manageable components.

Instead of asking “Analyze this marketing campaign and tell me how to improve it,” try “Before providing recommendations, first identify the campaign’s primary objectives, then evaluate how well each element supports those objectives, then identify the biggest gaps or opportunities, and finally provide specific, actionable recommendations.”

This approach works because it mirrors how human experts actually solve complex problems. By forcing the AI to show its work, you get more thoughtful, comprehensive results.

Component 6: Output Formatting Constraints

If you need the AI’s output to work with other tools or processes, you must specify the exact format you want. This is critical for anyone using AI as part of a larger workflow.

Don’t just ask for “a list of insights.” Specify: “Respond using only JSON format with the following keys: insight (string), supporting_evidence (array of strings), confidence_level (integer from 1–10), and recommended_action (string).”

Clear formatting constraints eliminate the back-and-forth that usually happens when AI output doesn’t match your needs.

Component 7: Delimiters and Structure

The final component involves using clear separators to distinguish different parts of your prompt. This helps the AI understand the hierarchy and relationship between different instructions.

Use markers like “### Instructions ###”, triple backticks for code or data, or XML tags to separate different sections. This is especially important for complex prompts with multiple components.

A well-structured prompt might look like:

### Role ###
[Your role definition]
### Task ###
[Specific instructions]
### Context ###
[Background information]
### Examples ###
[Sample inputs and outputs]
### Output Format ###
[Formatting requirements]

The Compound Effect of Better Prompting

When you start using this framework consistently, something interesting happens. You don’t just get better results from individual prompts, you start thinking more clearly about what you actually want from AI tools.

The process of breaking down your request into these seven components forces you to clarify your own thinking. Often, the act of writing a good prompt helps you realize what you’re really trying to accomplish.

“The best prompts I see are almost like project briefs,” a consultant who trains teams on AI adoption told me. “They’re clear about objectives, constraints, and success criteria. It’s good discipline whether you’re talking to AI or humans.”

Beyond ChatGPT: Why This Framework Matters

This framework isn’t just about getting better results from ChatGPT. It’s about developing a systematic approach to working with AI that will serve you as these tools become more powerful and more integrated into professional workflows.

As AI capabilities expand, the people who understand how to communicate effectively with these systems will have a significant advantage. The framework you learn today will apply to the AI tools you use tomorrow.

The Practice Makes Perfect Reality

Like any skill, effective prompting improves with practice. Start by applying this framework to a task you’re already doing with AI. Take a prompt that’s giving you mediocre results and rebuild it using all seven components.

You’ll probably be surprised by how much better the output becomes. More importantly, you’ll start to internalize the thinking process that leads to consistently good results.

The goal isn’t to memorize a template, it’s to develop the habit of thinking systematically about what you want from AI and how to communicate that effectively.

The Question That Changes Everything

Instead of asking “Why isn’t AI giving me good results?” start asking “What information does the AI need to give me exactly what I want?”

That shift in perspective changes everything. It moves you from being frustrated with AI limitations to being strategic about AI capabilities.

What’s the most important task you use AI for right now? And how would applying this seven-component framework change the results you get?

The difference between good and great AI output is often just a better prompt away.

--

--

Aakash Gupta
Aakash Gupta

Written by Aakash Gupta

Helping PMs, product leaders, and product aspirants succeed

Responses (1)