Why does this version of ChatGPT just feel like it’s extremely lazy and doesn’t want to actually do the tasks I ask it to do?

Understanding the Limitations of AI Tools: A User’s Experience with ChatGPT and Data Generation Tasks

In recent months, AI language models like ChatGPT have revolutionized the way individuals and businesses approach various tasks — from content creation to data analysis. However, as users delve into more specific and detailed projects, some encounter challenges that reveal the current limitations of these powerful tools.

A user recently shared their experience while attempting to generate a comprehensive list of their Magic: The Gathering (MTG) card collection using ChatGPT. Their goal was to create an organized spreadsheet with details such as rarity and color, and subsequently generate deck configurations based on this data.

Initially, they found that ChatGPT produced only a truncated list, showcasing roughly fifty cards instead of a complete inventory. When prompted to generate deck lists tailored to their collection (for example, a mono-colored red deck), the model often “hallucinated,” providing card suggestions that the user did not own or included cards from other colors, despite explicit instructions.

This experience highlights some common patterns with AI-driven tools today:

1. Data Limitations and Context Handling
   ChatGPT can struggle with processing extensive datasets or maintaining context across large inputs. As a result, when users upload or input extensive lists, the model may only generate a partial output or lose track of the full dataset.

2. Hallucination and Inaccuracy
   The phenomenon of AI “hallucination,” where models fabricate information or suggest nonexistent data, can be problematic, especially when precise accuracy is needed—such as in inventory management or deck building for collectible card games.

3. User-Driven Iteration Necessity
   To achieve desirable results, users often need to repeatedly prompt the model, offering clarifications or constraints. Despite this, the AI may still fall short of expectations, leading to frustration and perceptions of inefficiency.

4. Limitations of a Paid Service
   Many users pay a subscription fee (e.g., $20/month) for continued access to these AI tools, expecting reliable performance. When results do not meet expectations, it can be confusing or disappointing, prompting questions about the tool’s current capabilities.

Key Takeaways for Users and Developers
While AI language models represent remarkable technological advances, their effectiveness can vary significantly depending on the complexity of the task, dataset size, and precision requirements. Users should be aware that:

• For data-heavy or highly detailed tasks, supplementing AI outputs with manual oversight or traditional tools