Experimenting with LLM-Based Chunk Enhancement for Better RAG Results

Dec 1, 2023 ·

The article focuses on the development of a new type of artificial intelligence technology, called Deep Learning. Deep Learning is a form of machine learning that uses neural networks to learn from data and help computers simulate the functions of the human brain. The article explains how Deep Learning can be used to solve a variety of problems, such as image recognition, natural language processing, and more.

Deep Learning algorithms involve layers of neural networks that are trained with large amounts of data. By using these layers, computers can detect patterns in data and make accurate predictions. This means that Deep Learning algorithms can be used to identify objects in images, recognize text in documents, and understand speech. Additionally, Deep Learning algorithms can be used for predictive analysis, helping to forecast future events based on past data.

The article also discusses the potential applications of Deep Learning, such as autonomous vehicles, medical diagnosis, fraud detection, and more. In order to use Deep Learning for these tasks, models need to be trained using supervised learning techniques. This involves providing the models with labeled data, so that they can learn how to classify and predict outcomes accurately.

Finally, the article highlights some of the challenges associated with using Deep Learning. These include the time and cost involved in training the models, as well as the risk of bias if the training data is not representative of the real world. Despite these issues, Deep Learning is becoming increasingly popular and has already been successfully used in many applications.