How Chat GPT Works - Behind the Scenes

Are you curious to find out how CHAT GPT technically works? Have you heard about this revolutionary artificial intelligence technology that is changing the way we connect online. In this article, I will reveal the secrets of CHAT GPT, the virtual assistant based on Large Language Model (LLM) and Reinforcement Training from Human Feedback (RLHF).

In this guide I have collected months of tests and studies on a tool, which together with all the new things that are emerging in so-called LLMs, will change the way we work in the future.

CHAT GPT was launched on November 30, 2022, surprising the world with its rapid growth. In just two months, it reached 100 million monthly active users, surpassing even Instagram, which took two and a half years to reach the same result. This makes it the fastest growing app in history. But how does CHAT GPT reach such heights of success? Let's find out together.

It is important to note that although GPT-3.5 can generate quality text, it is critical to provide the model with adequate guidance to avoid generating content that is untruthful, toxic, or reflects harmful sentiments.

Further refinement of the model through techniques such as Reinforcement Training from Human Feedback (RLHF) helps to improve the quality and confidence of responses generated by Chat GPT.

To make the CHAT GPT model more secure, able to provide contextual responses and interact in the style of a virtual assistant, a process called Reinforcement Training from Human Feedback (RLHF) is used. This further training process transforms the basic model into a refined model that better meets user needs and aligns with human values.

RLHF involves collecting feedback from real people to create a "reward model" based on user preferences. This reward model serves as a guide for the model during training. It is similar to a cook practicing the preparation of dishes, following a reward model based on customers' taste. The cook compares the current dish with a slightly different version and learns which version is better according to the reward model. This process is repeated several times, allowing the cook to hone his cooking skills based on updated customer feedback.

The goal of PPO is to refine the decision policies of machine learning models, enabling them to learn from training data more efficiently. This is accomplished through a series of iterations, in which the model performs actions and compares the results with a slightly modified version of the decision policies. The algorithm evaluates the differences between the two policies and, based on those differences, updates the existing policies to bring the results closer to the desired performance.

PPO is distinguished by its ability to balance the exploration of new strategies with the use of information already learned from the model. This approach allows for better stability in training and greater efficiency in achieving optimal results. In addition, PPO offers advantages in terms of controlling changes made to decision policies, ensuring that changes are moderate and gradual, thus avoiding instability in learning and the occurrence of undesirable effects.

CHAT GPT represents an incredible breakthrough in artificial intelligence and natural language generation. Using LLMs such as GPT-3.5, trained on massive amounts of Internet data and subjected to RLHF, CHAT GPT is able to provide contextually relevant and semantically consistent responses to user queries.