Chain of Thought Trap - Why LLMs FAIL Without Complete Data

The Chain of Thought Trap: Why LLMs Fail Without Complete Data

In the world of AI development, we often fall into what I call the "Chain of Thought Trap" - expecting language models to perform complex reasoning with incomplete information. Let me walk you through this common misconception and what recent research reveals about it.

 The Illusion of AI Intelligence

When working with large language models (LLMs), we often believe that by filling our prompts with vast amounts of data through RAG (Retrieval-Augmented Generation) systems, we're providing all the necessary building blocks for complex reasoning. The reality is quite different.

Many developers imagine AI reasoning as a connected network that can magically bridge gaps between data points. We visualize water molecules of information connecting into intricate reasoning paths. But this mental model is fundamentally flawed.

The Missing Bridge Problem

Consider a common scenario: You have excellent historical financial data and want an investment strategy with 26% returns. You feed all this information to an AI and expect it to create a brilliant new investment approach.

But there's a crucial problem - you haven't provided the theoretical bridge between your historical data and your desired outcome. The AI isn't a general intelligence that can invent financial theorems or testing methodologies from thin air.

As I tell everyone: **Treat AI like a five-year-old child**. It can only work with what you explicitly provide.

 Creative but Failing Solutions

People have tried clever workarounds like:

- Thematic clustering

- Multi-dimensional subclustering

- Embedding structure analysis

All these approaches fail for the same fundamental reason - they don't address the missing connections between data points.

 Simple Solutions That Work

1. **Train your LLM on specific tasks** with gradually increasing complexity

2. **Define clear input parameters** for each specific task

3. **Create verification agents** to check logical dependencies between reasoning steps

The key is ensuring that for each step in your reasoning chain, all required parameters are available from previous steps. When parameter C is required but missing from earlier outputs, the AI has two choices:

- Admit it lacks information (which most aren't trained to do)

- Hallucinate a value for C to complete its trained reasoning pattern

 Real-World Evidence

Recent research from India compared LLM diagnostic performance with human experts for arthritis cases. The findings were striking:

- LLMs achieved 90-100% prediction accuracy

- Yet their reasoning was correct in only 10-40% of cases

- 40% of cases contained major reasoning flaws

This confirms that chain-of-thought reasoning we see in models isn't their actual reasoning process - it's just what they were trained to show us.

The Overthinking Problem

A fascinating April 2025 study from the University of Maryland and Lehigh University examined if AI models are "losing their critical thinking skills." They found:

- When asked simple questions with missing crucial information:

  - Non-reasoning models typically said "I need more context"

  - Reasoning models produced thousands of tokens trying to solve impossible problems

  - These models spent 300+ seconds generating 8,000+ tokens of circular reasoning

For example, when given a running calculation problem with a missing key premise:

- Simple models recognized missing data and asked for it

- Advanced reasoning models generated 4× more tokens, entered self-doubt loops, and tried to guess user intentions

This "overthinking" phenomenon appeared in all tested reasoning models, both reinforcement learning-based and supervised fine-tuned ones.

Conclusion

The AI isn't hallucinating as much as we think - it's trying to find values for data we've failed to provide. The fault lies not with the AI systems but with humans who don't provide the minimum necessary data for specific queries.

Don't fall into the AI trap of blaming the model when the real issue is incomplete input data. As we move forward in mid-April 2025, this understanding becomes increasingly crucial for effective AI implementation.