Crystal ball showing a future city skyline with financial charts overlaid, symbolizing AI forecasting.

ChatGPT for Forecasting: Can AI Predict the Future Without Extra Help?

Kai Mendoza in Tech & Innovation December 2025 • 4 min read.

"Explore how basic ChatGPT models use storytelling to forecast trends and events with surprising accuracy."

Artificial intelligence is rapidly evolving, pushing the boundaries of what machines can do. Large language models (LLMs) like OpenAI's ChatGPT-4 can now mimic human speech and perform complex tasks. These predictive machines may even provide new forecasting tools, but how accurate are they?

A new study explores the forecasting abilities of ChatGPT-3.5 and ChatGPT-4, focusing on how different prompting strategies impact accuracy. By testing the models' ability to predict events from 2022, using training data only up to September 2021, the researchers uncovered surprising insights into AI's predictive potential.

The researchers used two main prompting strategies: direct prediction and future narratives. Direct prediction prompts asked ChatGPT to forecast events directly. Future narratives involved ChatGPT telling fictional stories set after September 2021, with characters sharing events that had already happened. This approach tapped into the model's creativity to see if storytelling could unlock better predictions.

How Storytelling Enhances AI Forecasting

Crystal ball showing a future city skyline with financial charts overlaid, symbolizing AI forecasting.

The study found that future narrative prompts significantly improved ChatGPT-4's forecasting accuracy. When ChatGPT-4 was asked to tell stories about future events, its predictions became more accurate, especially for major Academy Award winners and economic trends. Direct prompts, on the other hand, often resulted in poorer performance.

Researchers analyzed 100 prompts and discovered that narrative prompts allowed ChatGPT-4 to leverage its ability to construct hallucinatory narratives. This capacity for narrative construction facilitated more effective data synthesis and extrapolation than straightforward predictions. It suggests that framing a request within a story helps AI process information more effectively.

Academy Awards: Future narratives significantly enhanced ChatGPT-4's ability to predict major Academy Award winners.
Economic Trends: Economic forecasts improved when the model impersonated public figures like Federal Reserve Chair Jerome Powell in narrative scenarios.
Falsification Exercise: Repeating experiments with more recent training data dramatically improved accuracy when the training window included the events being prompted for, achieving 100% accuracy in many instances.

In a falsification exercise conducted in May 2024, after the models had access to more recent training data, ChatGPT-4's accuracy dramatically improved. When the training window included the events being prompted for, the model achieved 100% accuracy in many instances. This suggests that ChatGPT-4's predictions in the initial experiments were based solely on its training data.

The Future of AI Predictions

This research highlights the potential of LLMs for predictive analysis and suggests that narrative prompts could be a key to unlocking more accurate AI forecasts. By framing predictive tasks within creative storytelling, it may be possible to access more sophisticated predictive capabilities, even in sensitive areas where direct forecasting might breach ethical considerations. This work opens new avenues for applying LLMs in economic forecasting, policy planning, and more, challenging how we interact with sophisticated AI models.

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Everything You Need To Know

How can ChatGPT be used for forecasting trends and events?

Basic ChatGPT models, specifically ChatGPT-3.5 and ChatGPT-4, can be used for forecasting by employing different prompting strategies. The models use storytelling or 'future narratives' to predict events and trends. Instead of directly asking for a prediction, ChatGPT is prompted to create a fictional story set in the future where characters discuss events that have already occurred. This method taps into the model's ability to construct narratives, leading to surprisingly accurate predictions, especially when using ChatGPT-4.

What are direct prediction prompts, and how do they compare to future narrative prompts when using ChatGPT for forecasting?

Direct prediction prompts involve directly asking ChatGPT to forecast an event. In contrast, future narrative prompts ask ChatGPT to create a story set after a specific date, where characters discuss events that have already happened. Research indicates that future narrative prompts significantly improve ChatGPT-4's forecasting accuracy compared to direct prompts. The narrative approach allows ChatGPT-4 to better leverage its ability to synthesize data and extrapolate trends, leading to more effective predictions in areas like economic trends and Academy Award winners. Direct prompts tend to yield poorer performance because they don't engage ChatGPT's narrative construction capabilities.

What role does storytelling play in enhancing the forecasting abilities of ChatGPT?

Storytelling, or the use of future narrative prompts, enhances ChatGPT-4's forecasting abilities by allowing the model to leverage its capacity to construct hallucinatory narratives. By framing a predictive task within a story, ChatGPT-4 can synthesize information and extrapolate trends more effectively than when using direct prediction prompts. This approach was particularly successful in predicting major Academy Award winners and economic trends. The act of creating a narrative appears to help the AI process information in a way that unlocks more sophisticated predictive capabilities.

How does the training data available to ChatGPT affect its ability to make accurate predictions?

The training data significantly impacts ChatGPT's predictive accuracy. A falsification exercise demonstrated that when the training window included the events being prompted for, ChatGPT-4 achieved 100% accuracy in many instances. This suggests that in initial experiments, ChatGPT-4's predictions were primarily based on the information it had already been trained on. Access to more recent and relevant data allows the model to make more informed and accurate forecasts, underscoring the importance of up-to-date training data for AI predictive models.

What are the potential future applications of using large language models like ChatGPT for predictive analysis, and what challenges or ethical considerations might arise?

Large language models (LLMs) like ChatGPT hold significant potential for predictive analysis in various fields, including economic forecasting and policy planning. The ability to frame predictive tasks within creative storytelling opens new avenues for accessing sophisticated predictive capabilities. However, challenges and ethical considerations may arise, particularly in sensitive areas where direct forecasting could breach ethical boundaries. Ensuring responsible and ethical use of these models will be crucial as they become more integrated into decision-making processes. Further research is needed to explore the full extent of their capabilities and address any potential risks.