Large language models (LLMs) have revolutionized how we interact with machines, but controlling their output remains a challenge. Imagine trying to steer a ship without a rudder – that's the current state of many LLMs. They generate impressive text, but often lack the precision and control needed for specific tasks. Inspired by the human brain's language centers, Broca's and Wernicke's areas, researchers have developed the BWArea model, a novel approach to controllable language generation. Just as Broca's area governs speech production based on cognitive decisions from Wernicke's area, the BWArea model treats language generation as a decision-making process. This model consists of three key components: a language world model, an inverse dynamics model, and a cognitive policy. The inverse dynamics model acts like Wernicke's area, deciphering the intent behind each word. The cognitive policy then guides the language world model to generate text that aligns with the intended meaning. This approach allows for fine-tuning based on reward metrics, making it easier to align the model with specific goals. Early results show the BWArea model outperforms traditional LLMs in several tasks, including text-based games and complex reasoning challenges. It also demonstrates resilience to noisy data, a common problem for current LLMs. While still in its early stages, the BWArea model offers a promising path towards more controllable and reliable AI. This research could lead to AI assistants that are not only fluent but also capable of following complex instructions and adapting to various scenarios. The challenge now lies in scaling the model and exploring its potential in diverse applications, from creative writing to personalized education.