Why is chatgpt so bad at creating output of e.g., “X” amount of observations and always comes back with partial number?

Understanding the Limitations of ChatGPT in Delivering Complete Data Sets

As many users of AI language models like ChatGPT discover, while these tools are invaluable for research and automation, they often fall short when tasked with generating comprehensive lists or specific quantities of data. A common frustration involves prompting ChatGPT to compile a certain number of observations—say, details on twenty companies—and receiving incomplete results instead.

For example, requesting ChatGPT to review twenty companies and tabulate their headquarters often results in only partial outputs. It might list only ten companies initially, then, upon prompting for the full list, it responds with fewer than requested or repeats earlier results. This cycle can repeat multiple times before the desired amount of data is obtained, which can be both time-consuming and perplexing.

So, what’s behind this behavior? Several factors contribute:

1. Token Limitations: ChatGPT operates within a maximum token count per response. When asked for large datasets, it might truncate responses to stay within these bounds.

2. Context Management: The AI doesn’t retain context beyond the current conversation turn efficiently, especially if prompts are not structured to guide complete outputs.

3. Generation Constraints: The model aims for concise and relevant responses, which can lead it to omit items when prompted for extensive lists, anticipating that the user may want summarized or key information.

4. Inherent Randomness: Due to its probabilistic nature, ChatGPT may generate varying outputs across different attempts, especially when dealing with extensive data.

Practical Tips for Better Results:

• Break down your requests into smaller parts. Instead of asking for all twenty items at once, request batches of five or ten.
• Use iterative prompting, gradually building up the complete dataset.
• Incorporate clear instructions, such as “Please list all twenty companies’ headquarters in a single table,” to set expectations.
• Consider supplementing AI outputs with external data sources to ensure completeness.

Despite these limitations, understanding how ChatGPT processes and responds to large, data-intensive queries can help users optimize their interactions. Patience and strategic prompting are key to leveraging AI tools effectively in research and data compilation projects.