Two weeks ago, I explored a basic method for error-checking the output of Large Language Models (LLMs). Just a week later, OpenAI unveiled their latest model, o1 (Strawberry)—the first frontier model boasting built-in reasoning and iterative correction. This isn’t merely another version update; o1 signifies a fundamental shift in how we conceptualize reasoning within AI models and how we engage with them.

The Evolution of AI Reasoning

The arrival of o1 is exciting for two primary reasons. First, it incorporates a “Chain of Thought” mechanism, effectively deconstructing user prompts into logical steps to generate more accurate answers. Second, it utilizes this chain of thought to verify its own outputs—a more advanced iteration of the error-checking approach I discussed in my previous article.

But what does this mean in practical terms? Ethan Mollick’s crossword example provides a clear illustration. When solving a crossword puzzle, multiple answers may fit a given clue, but only one will integrate with the rest of the puzzle. For instance, A red or green fruit that is often enjoyed pressed as an alcoholic drink (5 letters) could be either GRAPE or APPLE, but only one will align correctly with surrounding answers.

Most LLMs would generate GRAPE and move forward without re-evaluating its fit within the broader puzzle. In contrast, o1 excels by looping back, much like a human would, to recognize that GRAPE doesn’t fit and subsequently correct it to APPLE.