The AI taxonomy in the context of this
Posted: Tue Feb 11, 2025 7:16 am
RAG (Retrieval Augmented Generation) Architecture for Data Quality AssessmentBy Prashanth Southekal, Tobias Zwingmann, and Arun Marar on July 12, 2024
Read more about Prashanth Southekal, Tobias Zwingmann, and Arun Marar.
A large language model (LLM) is a type of artificial intelligence (AI) solution that can recognize and generate new content or text from existing content. It is estimated that by 2025, 50% of canada whatsapp number data digital work will be automated through these LLM models. At their core, LLMs are trained on large amounts of content and data, and the architecture of LLMs primarily consists of multiple layers of neural networks, like recurrent layers, feedforward layers, embedding layers, and attention layers. These layers work together to process the input content and generate coherent and contextually relevant text. In this backdrop, the terms large language models (LLMs) and generative AI are often used interchangeably. Generative AI (GenAI), on the other hand, refers to a broader category of AI models designed to create new content that is not only text, but also includes images, audio, and video. blog post is as shown below.
Figure AI Taxonomy
LLMs such as OpenAI’s GPT-4, Google’s Gemini, and Anthropic’s Claude have become very popular among the general internet audience, especially when consumed via easy-to-use interfaces like ChatGPT for getting fast answers to queries such as “Who is the first president of the U.S.?” However, the corporate usage of these LLM models for queries such as “What is the dollar value of the cost of poor data quality in purchase orders issued in 2022?” has been much slower. What are the reasons for this? Broadly, the possible issues fall into two main categories.
Read more about Prashanth Southekal, Tobias Zwingmann, and Arun Marar.
A large language model (LLM) is a type of artificial intelligence (AI) solution that can recognize and generate new content or text from existing content. It is estimated that by 2025, 50% of canada whatsapp number data digital work will be automated through these LLM models. At their core, LLMs are trained on large amounts of content and data, and the architecture of LLMs primarily consists of multiple layers of neural networks, like recurrent layers, feedforward layers, embedding layers, and attention layers. These layers work together to process the input content and generate coherent and contextually relevant text. In this backdrop, the terms large language models (LLMs) and generative AI are often used interchangeably. Generative AI (GenAI), on the other hand, refers to a broader category of AI models designed to create new content that is not only text, but also includes images, audio, and video. blog post is as shown below.
Figure AI Taxonomy
LLMs such as OpenAI’s GPT-4, Google’s Gemini, and Anthropic’s Claude have become very popular among the general internet audience, especially when consumed via easy-to-use interfaces like ChatGPT for getting fast answers to queries such as “Who is the first president of the U.S.?” However, the corporate usage of these LLM models for queries such as “What is the dollar value of the cost of poor data quality in purchase orders issued in 2022?” has been much slower. What are the reasons for this? Broadly, the possible issues fall into two main categories.