Business Process Modeling Language (BPML) is a language used for modeling business processes, enabling the design and implementation of process-based applications. Important for defining complex business processes and ensuring their effective implementation in digital products.
Business Process Execution Language (BPEL) is a language for specifying business process behaviors based on web services. Important for defining and automating complex business processes in digital product workflows.
Natural Language Processing (NLP) is a field of AI focused on the interaction between computers and humans using natural language. Essential for developing applications like chatbots, language translation, and sentiment analysis.
The study of the nature, structure, and variation of language, including phonetics, phonology, syntax, semantics, and pragmatics. Essential for understanding how language influences communication and user interactions in digital products.
Large Language Model (LLM) is an advanced artificial intelligence system trained on vast amounts of text data to understand and generate human-like text. Essential for natural language processing tasks, content generation, and enhancing human-computer interactions across various applications in product design and development.
A type of model architecture primarily used in natural language processing tasks, known for its efficiency and scalability. Essential for state-of-the-art NLP applications.
A method in natural language processing where multiple prompts are linked to generate more complex and contextually accurate responses. Essential for enhancing the capability and accuracy of AI models in digital products that rely on natural language understanding.
A component in neural networks that allows the model to focus on specific parts of the input, improving performance. Essential for developing advanced AI models, particularly in natural language processing.
Conversational User Interface (CUI) is a user interface designed to communicate with users in a conversational manner, often using natural language processing and AI. Essential for creating intuitive and engaging user experiences in digital products.
AI systems designed to communicate with users through natural language, enabling human-like interactions. Crucial for developing advanced customer service and user engagement solutions.
The use of natural language processing to identify and extract subjective information from text, determining the sentiment expressed. Crucial for understanding public opinion and customer feedback.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
An AI model that has been pre-trained on a large dataset and can be fine-tuned for specific tasks. Essential for developing state-of-the-art NLP applications.
The process of linking language to its real-world context in AI systems, ensuring accurate understanding and interpretation. Crucial for improving the relevance and accuracy of AI-generated responses.
Numeronym for the word "Localization" (L + 10 letters + N), adapting a product or content to meet the language, cultural, and regional preferences of a specific target market. Essential for ensuring product relevance in different regions.
Numeronym for the word "Internationalization" (I + 18 letters + N), enabling localization for different languages, regions, and cultures without requiring extensive rework. Important for expanding product reach to global markets.
A machine learning-based search engine algorithm used by Google to help process search queries and provide more relevant results. Important for understanding modern SEO practices and how search engines interpret and rank web content.
The process of designing and refining prompts to elicit accurate and relevant responses from AI models. Crucial for optimizing the performance of AI applications.
A simplified, informal language used to describe the logic and steps of an algorithm or program, without syntax of actual programming languages. Useful for planning and communicating algorithms and program structures before implementation in digital product development.
The process of training an AI model on a large dataset before fine-tuning it for a specific task. Crucial for building robust AI models that perform well on various tasks.
Interference in the communication process caused by ambiguity in the meaning of words and phrases, leading to misunderstandings. Crucial for designing clear communication channels and reducing misunderstandings in user interactions.
Model-Based Systems Engineering (MBSE) is a methodology that uses visual modeling to support system requirements, design, analysis, and validation activities throughout the development lifecycle. Essential for managing complex systems, improving communication among stakeholders, and enhancing the overall quality and efficiency of systems engineering processes.
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
Voice User Interface (VUI) is a system that allows users to interact with a device or software using voice commands. Essential for creating hands-free, intuitive user experiences.
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
Generative Pre-trained Transformer (GPT) is a type of AI model that uses deep learning to generate human-like text based on given input. This technology is essential for automating content creation and enhancing interactive experiences.
Software agents that can perform tasks or services for an individual based on verbal commands. Crucial for enhancing user experience through hands-free interaction and automation.
The underlying goal or motivation behind a user's search query, crucial for understanding and optimizing content to meet user needs and improve SEO. Essential for creating content that aligns with user needs and improving search engine rankings.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
The process of anticipating, detecting, and resolving errors in software or systems to ensure smooth operation. Important for creating reliable and user-friendly software applications.
Fundamental guidelines that inform and shape the design process, ensuring consistency, usability, and effectiveness in product creation. Essential for creating coherent, user-centered designs that align with organizational goals and user needs.
A set of algorithms, modeled loosely after the human brain, designed to recognize patterns and perform complex tasks. Essential for developing advanced AI applications in various fields.
Model-View-Controller (MVC) is an architectural pattern that separates an application into three main logical components: the Model (data), the View (user interface), and the Controller (processes that handle input). Essential for creating modular, maintainable, and scalable software applications by promoting separation of concerns.
Computer programs designed to simulate conversation with human users, especially over the internet. Crucial for automating customer service and enhancing user engagement.
Behavior-Driven Development (BDD) is a software development approach where applications are specified and designed by describing their behavior. Important for ensuring clear communication and shared understanding between developers and stakeholders.
The use of algorithms to generate new data samples that resemble a training dataset, often used in AI for creating realistic outputs. Important for developing creative and innovative solutions in digital product design, such as content generation and simulation.
Search Engine Optimization (SEO) is the process of improving a website's visibility and ranking in organic search engine results. Essential for attracting more traffic and enhancing the online presence of a website.
Retrieval-Augmented Generation (RAG) is an AI approach that combines retrieval of relevant documents with generative models to produce accurate and contextually relevant responses. Essential for improving the accuracy and reliability of AI-generated content.
Perceivable, Operable, Understandable, and Robust (POUR) are the four main principles of web accessibility. These principles are essential for creating inclusive digital experiences that can be accessed and used by people with a wide range of abilities and disabilities.
A programming paradigm that uses objects and classes to structure software design, promoting reusability and scalability. Crucial for developing maintainable and scalable software systems.
Domain-Driven Design (DDD) is an approach to software development that focuses on modeling the business domain and its logic. Essential for aligning software development with business needs and creating maintainable systems.
A dark pattern where the user is guilt-tripped into opting into something by using language designed to shame them if they decline. Designers must avoid this manipulative tactic and respect user decisions without using guilt or shame.
The process of integrating knowledge into computer systems to solve complex problems, often used in AI development. Important for developing intelligent systems that can perform complex tasks and support decision-making in digital products.
A programming paradigm aimed at improving the clarity, quality, and development time of software by using structured control flow constructs. Essential for writing clear, maintainable, and efficient code in digital product development.
A search method that seeks to improve search accuracy by understanding the contextual meaning of terms in a query rather than just matching keywords. Important for understanding modern search algorithms and optimizing content accordingly.
A method used in AI and machine learning to ensure prompts and inputs are designed to produce the desired outcomes. Essential for improving the accuracy and relevance of AI responses.
A developer proficient in both front-end and back-end technologies, capable of building complete web applications. Crucial for delivering comprehensive and cohesive digital products by managing both user interface and server-side components.
In AI and machine learning, a prompt that specifies what should be avoided or excluded in the generated output, guiding the system to produce more accurate and relevant results. Crucial for refining AI-generated content by providing clear instructions on undesired elements, improving output quality and relevance.
An AI-driven assistant or tool that helps users accomplish tasks more efficiently, often by providing suggestions and automating routine actions. Important for enhancing productivity and user experience through AI assistance.
Interactive Voice Response (IVR) is an automated telephony system that interacts with callers, gathers information, and routes calls to the appropriate recipient. It improves customer service and automates information retrieval.
AI as a Service (AIaaS) is a service model where AI tools and algorithms are provided over the internet by a third-party provider. Essential for making advanced AI capabilities accessible to businesses.
A parameter that controls the randomness of AI-generated text, affecting creativity and coherence. Important for fine-tuning the behavior and output of AI models.