From GPT to RAG: Decoding the Latest AI terminology 

Advanced technology brings a new vocabulary. This glossary breaks down essential AI terms, helping you navigate the tools and concepts powering the next wave of business transformation.  

Agentic AI 

AI systems that are capable of autonomously pursuing complex multi-step goals with minimal human intervention. This development marks a move toward more independent AI operations, empowering organizations to scale decision-making and handle intricate workflows with greater ease.  

By leveraging agentic AI, businesses can automate tasks that require adaptability and foresight, such as dynamic resource allocation and real-time strategy adjustments. For instance, in supply chain management, agentic AI can predict demand fluctuations and adjust orders accordingly, reducing waste and optimizing inventory levels. 

AI Copilot 

A conversational interface designed to assist users in completing tasks, analyzing data and making decisions across various enterprise domains.    

By automating routine processes and supporting strategic functions, AI copilots enhance productivity and efficiency. For instance, in software development, AI copilots can suggest code snippets, detect bugs and optimize performance, thereby accelerating the development cycle. In customer service, AI copilots can provide agents with real-time information and suggested responses, improving response times and customer satisfaction.  

Computer Vision 

Computer vision enables AI systems to interpret and understand visual data from the world, such as images or videos. This field includes tasks like object detection and scene analysis, which are critical for applications in security, healthcare and autonomous vehicles.  

Advancements in deep learning have significantly improved the accuracy and efficiency of computer vision systems, enabling real-time processing and analysis of complex visual data. However, challenges such as data privacy, bias in training data, and the need for large annotated datasets remain areas of active research and development. 

Extractive AI vs. Generative AI 

Extractive AI focuses on extracting and summarizing relevant information from existing datasets or texts, often utilizing natural language processing techniques to identify key elements. Conversely, Generative AI involves creating new and original content—such as text, images, or music — by learning patterns and generating outputs not explicitly present in the training data.  

Generative AI models, like OpenAI's DALL·E, can produce novel images based on textual descriptions, showcasing creativity beyond mere data retrieval. While extractive AI is valuable for information retrieval and summarization tasks, generative AI opens new possibilities in creative industries, content generation and personalized user experiences. However, the potential for misuse, such as generating deepfakes or misinformation, necessitates careful consideration of ethical implications and the development of robust safeguards. 

Generative Pre-trained Transformer (GPT) 

GPT models, such as GPT-4, employ transformer architecture to generate human-like text, performing a wide range of tasks. These models are continuously evolving, with recent versions offering enhanced capabilities in language understanding and multimodal data processing.  

GPT-4, for example, can interpret both text and images, enabling more nuanced interactions and applications across various domains. The pre-training phase involves training on vast datasets to learn language patterns, followed by fine-tuning for specific tasks, allowing GPT models to adapt to diverse applications such as translation, summarization and question-answering. 

Grounding 

Anchoring AI models in verified knowledge, ensuring their responses are based on accurate, real-world data. This process is crucial for minimizing hallucinations — instances where AI generates plausible but incorrect information—thereby increasing trustworthiness and providing reliable information in high-stakes environments like healthcare diagnostics and legal advisories.  

Techniques for grounding include integrating external knowledge bases, implementing retrieval-augmented generation, and employing fact-checking mechanisms. Effective grounding enhances the credibility of AI systems and builds user trust, which is essential for their widespread adoption. 

Hallucinations 

The generation of fictitious or inaccurate content by AI models, often resulting from their probabilistic nature and limitations in training data. These can manifest as incorrect facts or nonsensical statements, posing challenges in applications where accuracy is paramount. Addressing hallucinations involves refining training data and implementing grounding techniques to align AI outputs with factual information.  

Researchers are exploring methods such as reinforcement learning from human feedback and incorporating explicit knowledge graphs to reduce the occurrence of hallucinations. Understanding and mitigating hallucinations is critical for deploying AI systems in sensitive domains like healthcare, finance, and law. 

Knowledge Graph 

A structured representation of information that reveals relationships between entities. By providing context and understanding connections, it enables AI systems to deliver more accurate and insightful results, particularly in search engines and recommendation systems.  

For example, Google's Knowledge Graph enhances search results by connecting related concepts, offering users a more comprehensive understanding of their queries.    

Knowledge graphs are constructed using data from various sources, including structured databases and unstructured text, and are continuously updated to reflect new information. They play a vital role in enabling semantic search, question-answering systems, and personalized recommendations by capturing the complexities of real-world knowledge. 

Large language models (LLMs) 

An advanced AI model that can understand, generate, and manipulate human language. LLMs, such as OpenAI's GPT-4 or Google’s PaLM2, are trained on massive amounts of text data to generate contextually relevant responses based on user inputs.  

LLMs are static based on the data first used to train them, which means that they only know what they know; new information must be added via fine-tuning, embeddings or prompts. These models are part of a broader family of foundational models which include the ability to process other types of content like images.  

Multimodal AI 

Multimodal AI can process and interpret diverse data types — including text, images, audio and video — enabling more comprehensive applications. This technology broadens the scope of AI capabilities, making it highly adaptable across industries from healthcare to entertainment.  

For instance, a multimodal AI system can analyze medical images alongside patient records to provide more accurate diagnoses. In entertainment, multimodal AI can generate immersive experiences by combining visual, auditory and textual elements, enhancing user engagement.   

Natural language processing (NLP)  

A branch of AI that enables computers to understand and interpret human language. Enterprise organizations use NLP to make sense of unstructured data, such as analyzing large amounts of customer feedback to identify problems and trends. 

Applications of NLP include sentiment analysis, language translation and chatbots, facilitating more natural interactions between humans and machines. In marketing, NLP analyzes social media sentiments to inform campaign strategies, while in healthcare, it extracts insights from clinical notes to improve patient care. 

Prompt engineering 

The practice of designing and refining input queries to guide AI models, particularly large language models (LLMs), toward generating precise and contextually appropriate outputs. By carefully crafting prompts, users can influence the behavior of AI systems to meet specific objectives, thereby maximizing the utility of language models in various applications.  

This process involves understanding the model's capabilities and limitations, as well as the nuances of natural language, to formulate prompts that elicit desired responses. Effective prompt engineering is crucial in fields such as content creation, customer service and data analysis, where tailored AI outputs can enhance productivity and decision-making.  

Retrieval-Augmented Generation (RAG) 

An AI framework that combines the generative capabilities of large language models with the precision of information retrieval systems. In this approach, the AI model retrieves relevant external knowledge — such as documents, databases or real-time information — and incorporates it into its response generation process.  

This integration allows AI systems to produce more accurate, up-to-date and contextually relevant outputs, making RAG particularly valuable in dynamic business environments where information is continually evolving. For instance, in customer support, RAG can enable AI to provide solutions based on the latest product documentation, thereby improving service quality and efficiency.  

Vector database 

A specialized data storage system designed to manage and retrieve high-dimensional vectors that represent various data elements, including text, images and audio. In AI and machine learning, data is often transformed into vector representations — numerical arrays that encode the attributes and semantics of the original data.  

For example, word embeddings are vectors that capture the meanings and relationships of words in a continuous vector space. Vector databases facilitate efficient similarity searches, clustering and other operations by enabling rapid comparison of these high-dimensional vectors. This capability is essential in applications like recommendation systems, image recognition and natural language processing, where identifying similar items or patterns is crucial.  

Word embeddings 

Dense, continuous vector representations of words that capture their meanings, semantic relationships, and contextual nuances.  

Unlike one-hot encoding, which represents words as sparse vectors with limited relational information, word embeddings position words in a continuous vector space where semantically similar words are located near each other. This spatial arrangement allows AI models to understand and process language more effectively, facilitating tasks such as language translation, sentiment analysis and information retrieval.