GPT for

Question answering in the context of Natural Language Understanding (NLU) involves using machine learning algorithms and NLP techniques to understand natural language questions and provide relevant and accurate answers. The system analyzes the question and searches for relevant information from a knowledge base or corpus of text, and then generates an answer that is most appropriate based on the context and language used in the question. This technology is useful for a wide range of applications, including customer support, virtual assistants, and information retrieval systems.

Prompting and instructing in the context of Large Language Models (LLMs) involves providing a specific context or task to the language model and asking it to generate relevant text based on that input. This is done by providing a set of instructions or a prompt that specifies the desired output, and the language model generates text based on that prompt. This technology can be used for a variety of applications, including text generation, chatbots, and language translation, among others. The idea is to provide a more human-like interaction with the system, where the user can provide specific input and receive an appropriate response generated by the language model, e.g. "Extract key figures from the report and summarize them".

IDP, or Intelligent Document Processing, refers to the use of NLU and machine learning algorithms to automatically extract and classify information from various types of documents, such as invoices, contracts, and forms, to reduce the time and effort needed for manual processing and improve the accuracy and efficiency of data extraction. An example is removing sensitive user informations from text.