Building Thinking Models with CoT

Chain-of-thought simply asks the models to show its reasoning before arriving to an answer that eliminates the “immediate jump to conclusion” step taken by most LLMs.

This steps eliminates - “Hallucinations“ therefore breaking the problem statement into smaller logical steps which allows it to become better in reasoning and calculating responses.

It does not inherently “think” like a human does - however it stimulates a reasoning process which improves :

1. Accuracy

2. Precision

3. Transparency

Using this we can convert any model from, auto answering mode to problem-solving mode