AI in Investment Research: Reasoning vs Information Retrieval

There's a common misconception about AI, especially with Generative AI and LLMs: that these systems can handle tasks that require reasoning. At stockinsights.ai, we often get asked if our AI can help with investment research workflows that involve making complex decisions. To answer that, we need to clarify the difference between reasoning and information retrieval.

Information Retrieval: The Basics

Information retrieval is simple. It’s like pulling a file from a well-organized cabinet. When an AI tells you that the capital of India is New Delhi, it’s just fetching a fact from its database.

In investment research, this ability is incredibly useful. Our AI can quickly pull relevant data from financial filings, earnings reports, and market data. But let’s be clear—this isn’t reasoning.

Reasoning: Beyond the Basics

Reasoning is different. It’s about thinking things through, making connections, and coming up with new insights. For example, if you know that apples fall from trees and gravity pulls things downward, you reason that gravity makes apples fall.

In finance, reasoning involves understanding market dynamics, making predictions, and assessing how economic changes might affect a company. This is more than just retrieving data—it’s about applying and interpreting information in new ways.

The AI Misconception

Many people think LLMs can reason because they can summarize documents, answer questions, or generate reports. But what they’re actually doing is advanced information retrieval. They pull from a vast knowledge base and reorganize that information to fit your question.

But when faced with tasks that require real logical thinking, LLMs struggle. For instance, they often have trouble solving logic puzzles or understanding abstract concepts. This shows they’re good at recognizing patterns but not at true reasoning.

The Human Edge

Humans have the ability to reason from first principles. We learn, understand, and apply rules in ways that go beyond just pulling data. We don’t just know that 9+2 equals 11; we understand why it does. This kind of reasoning is something LLMs can’t do yet.

The Intersection

Reasoning and information retrieval are linked but different. Information retrieval is about getting known data, while reasoning turns that data into new insights. It’s like a chef who uses ingredients to create a unique dish. At stockinsights.ai, we use this understanding to refine our AI tools to support better investment research. Our models excel at pulling and organizing data, making it easier for analysts to dive deeper.

Conclusion

In investment research, it’s crucial to know the difference between reasoning and information retrieval to use AI effectively. While LLMs are powerful for data retrieval and analysis, true reasoning is still something only humans can do. As AI technology advances, we’re committed to leveraging its strengths while recognizing its current limits.