Think Fast: Unlocking the Power of AI Reasoners

In our first installment of the “I will Teach You to AI” series, we’ll explore reasoning AI—how it works, when to use it, and when other AI models are a better fit. While reasoners excel at structured, logical problem-solving, they are not always the best tool for the job.

What Are AI Reasoners?

Reasoning AIs, or reasoners, are AI models designed to draw logical conclusions, recognize relationships, and make decisions based on structured logic. Unlike generative AI models (such as GPT-4o or Claude 3.5), which rely on pattern recognition and probabilities, reasoners follow a “chain-of-thought” approach.

Inspired by Daniel Kahneman’s System 2 thinking (slow, logical reasoning), reasoners analyze data step by step, ensuring logical consistency rather than responding based on probability.

Example Models: OpenAI’s o1 and o3-mini are reasoning models trained with reinforcement learning to handle complex problem-solving, coding, scientific analysis, and multi-step planning.

Unlike typical generative AI models (such as non-reasoning ChatGPT-4o or Claude Sonnet 3.5), which are based on patterns and probabilities, reasoners follow a clearly defined, logic-based approach, namely “Chain-of-thought.” They rely not on statistical probability but on unambiguous rules and facts.

Reasoning models, like OpenAI o1 and o3-mini, are new large language models trained with reinforcement learning to perform complex reasoning. Reasoning models think before they answer, producing a long internal chain of thought before responding to the user. Reasoning models excel in complex problem solving, coding, scientificreasoning, and multi-step planning for agentic workflows