If you’ve been using AI tools like ChatGPT, Claude, or even more advanced options like Deep Research, you might have noticed something curious: your results can vary dramatically. Sometimes these tools seem almost magical, and other times they fall flat. What if I told you this difference isn’t just about which AI model you’re using, but how you’re using it?

The performance of AI tools follows what experts call an S-curve pattern. Understanding where you stand on this curve can transform how you use these tools, and the results you get from them.

What Is the AI Performance S-Curve?

Imagine a graph where the horizontal axis represents your prompting skill level, and the vertical axis shows the performance you get from an AI model. The relationship between these two creates an S-shaped curve with three distinct sections:

The first flat section is where most people start. Here, your prompting skills are basic, but you still get decent results because modern AI models are designed to be helpful even with minimal guidance. You might ask simple questions and get satisfactory answers – nothing groundbreaking, but useful enough.

The middle section – the steep slope – is where things get interesting. This is the zone where small improvements in your prompting skills lead to dramatic jumps in performance. Learning just a few techniques here can double or triple your results.

The final flat section represents the ceiling of what a particular AI model can do. Even with expert-level prompting, there’s a limit to what each model can achieve. But this ceiling rises with each new generation of AI.

Where Do You Stand on the Curve?

Take a minute to reflect on your experience with AI tools. Do any of these sound familiar?

You’re in the first flat section if:

• You tend to ask one-line questions
• You get frustrated when the AI doesn’t understand what you want
• You often need to repeat yourself or clarify your requests
• You view AI as a slightly better search engine

You’re climbing the slope if:

• You’ve started giving context before asking questions
• You specify the format you want answers in
• You’ve experimented with different ways of phrasing the same question
• You notice that how you ask matters as much as what you ask

You’re approaching the upper plateau if:

• You customize instructions based on the specific task
• You break complex problems into manageable chunks
• You know which techniques work best for different types of tasks
• You can predict when AI will struggle and adjust accordingly

Most people never make it past the first section. They try an AI tool, get some basic value, and assume that’s all there is to it. But the real power lies in that middle section – the slope.

Moving Up the Curve

The good news? You don’t need a technical background to climb this curve. Here are some practical steps to level up:

Start by being more specific. Instead of asking “Write me a blog post,” try “Write a blog post about sustainable gardening practices for urban apartments, focusing on low-maintenance plants and water conservation techniques.”

Provide relevant context. If you’re working on a marketing strategy, let the AI know about your target audience, company values, and goals before asking for suggestions.

Tell the AI how to think. For complex problems, ask it to “think step by step” or “consider different perspectives before answering.” This dramatically improves its reasoning.

Request examples. When learning something new, ask for concrete examples. Abstract concepts become clearer when illustrated.

Use feedback loops. If you don’t get what you need the first time, don’t start over. Instead, tell the AI what’s missing or what you’d like to change about its response.

The Practice Makes Progress Principle

Moving up this curve isn’t an overnight transformation. It requires practice, experimentation, and a willingness to learn from both successes and failures.

What I’ve found most valuable is keeping a prompt journal – a simple record of which prompts worked well and which ones flopped. Over time, patterns emerge that reveal what techniques work best for specific types of tasks.

Try setting aside time each week to experiment with new prompting approaches. Test different formats, instruction types, and ways of breaking down complex problems. The skills you develop will transfer across different AI models and tools.

Remember that even AI experts are constantly refining their approach as models evolve. The learning curve is part of the journey for everyone – not a sign that you’re doing something wrong.

Because when it comes to AI, it’s not just about having access to the best tools – it’s about knowing how to use them effectively. And that knowledge is something you build through consistent, intentional practice.