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Originally published on Towards AI.

The Building Blocks For An Autonomous Agent In Prediction Markets? 🤔
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As someone who works with vector databases daily, I’ve become accustomed to the conventional applications of Retrieval-Augmented Generation (RAG) in scenarios such as extracting information from dense user manuals, navigating complex code bases, or conducting in-depth legal research. These “talk to your documents” use cases, while impressive, often revolve around similar challenges across different datasets, which can become somewhat monotonous.

So, it was particularly refreshing when I came across the paper Approaching Human-Level Forecasting with Language Models by researchers Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt from UC Berkeley. They propose a novel (at least to me) use of RAG: forecasting events!

In this blog post, we’ll take a detailed walk through of a sample prediction from start to finish to understand how the system employs prompt engineering to drive its predictions and conclude with a brief overview of the results.

Imagine it’s now June 15, 2023, and you’re an avid Reddit user. Naturally, you’re keenly interested in the following question:

Will Reddit announce changes or a delay to its proposed API fee pricing before July 1, 2023?

and to give you a bit more context, here’s a brief overview of the situation:

In April… Read the full blog for free on Medium.

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Published via Towards AI