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.
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 of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
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.
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.
A tree-like model of decisions and their possible consequences, used in data mining and machine learning for both classification and regression tasks. Valuable for creating interpretable models in digital product design and user behavior analysis.
A statistical method used to predict a binary outcome based on prior observations, modeling the probability of an event as a function of independent variables. Essential for predicting categorical outcomes in digital product analysis and user behavior modeling.
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.
A theoretical framework in economics that assumes individuals act rationally and seek to maximize utility, used to predict economic behavior and outcomes. Important for understanding traditional economic theories and designing systems that account for rational decision-making.
Capability, Opportunity, Motivation (COM...) is a framework for understanding Behavior (Ã B). Important for designing interventions that effectively change user behavior.
A framework that outlines how a product is developed, managed, and delivered, including roles, processes, and tools used throughout its lifecycle. Crucial for ensuring efficient and effective product management and development.
A psychological model that outlines the stages individuals go through to change behavior, including precontemplation, contemplation, preparation, action, and maintenance. Crucial for designing interventions and experiences that support users at different stages of behavior change.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments. Crucial for ensuring the reliability, scalability, and performance of AI systems throughout their lifecycle, bridging the gap between model development and operational implementation.
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
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.
Attention, Interest, Desire, Action (AIDA) is a marketing model that outlines the stages a consumer goes through from awareness to decision. Crucial for creating effective marketing strategies and campaigns.
A cognitive architecture model that explains how humans can learn and adapt to new tasks. Useful for understanding user learning and behavior adaptation, informing better user experience design.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
A design process model that outlines four phases: Discover, Define, Develop, and Deliver, promoting both divergent and convergent thinking. Crucial for structuring the design process and fostering both creativity and precision.
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.
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.
A form of regression analysis where the relationship between the independent variable and the dependent variable is modeled as an nth degree polynomial. Useful for modeling non-linear relationships in digital product data analysis.
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.
Software as a Service (SaaS) is a software distribution model where applications are hosted by a service provider and accessed over the Internet. Crucial for enabling scalable and cost-effective software solutions for users.
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.
The process of designing and refining prompts to elicit accurate and relevant responses from AI models. Crucial for optimizing the performance of AI applications.
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.
A model by Don Norman outlining the cognitive steps users take when interacting with a system: goal formation, planning, specifying, performing, perceiving, interpreting, and comparing. Important for designing user-friendly and effective products by understanding and supporting user behavior at each stage.
Systematic errors in AI models that arise from the data or algorithms used, leading to poor outcomes. Important for ensuring fairness and accuracy in AI systems.
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.
Business-to-Business-to-Consumer (B2B2C), a business model where businesses sell products or services to other businesses that then sell them to consumers. Important for understanding complex value chains and partnership strategies.
Business-to-Business (B2B), a business model where products or services are sold from one business to another. Crucial for understanding business markets and developing inter-business strategies.
The process of self-examination and adaptation in AI systems, where models evaluate and improve their own outputs or behaviors based on feedback. Crucial for enhancing the performance and reliability of AI-driven design solutions by fostering continuous learning and improvement.
Business Process Model and Notation (BPMN) is a graphical representation for specifying business processes in a workflow, using standardized symbols and notations. Essential for creating clear, standardized diagrams that facilitate understanding and communication of business processes in digital product design.
A statistical technique that uses several explanatory variables to predict the outcome of a response variable, extending simple linear regression to include multiple input variables. Crucial for analyzing complex relationships in digital product data.
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.
Business-to-Business-to-Business (B2B2B), a business model where businesses sell products or services to other businesses that then sell them to additional businesses. Crucial for understanding multi-tiered business relationships and strategies.
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 product that significantly changes the market or industry by introducing innovative features or a new business model. Important for understanding market dynamics and identifying opportunities for innovation.
A metric that shows the revenue that a company can expect to receive annually from its customers for subscriptions or services. Essential for understanding business performance and growth potential.
A mathematical framework used to analyze strategic interactions where the outcomes depend on the actions of multiple decision-makers. Useful for designing systems and processes that involve competitive or cooperative interactions.
A statistical method that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Essential for predicting outcomes and understanding relationships between variables in digital product design and analysis.
The process of using statistical analysis and modeling to explore and interpret business data to make informed decisions. Essential for improving business performance, identifying opportunities for growth, and driving strategic planning.
A structured framework for organizing information, defining the relationships between concepts within a specific domain to enable better understanding, sharing, and reuse of knowledge. Important for creating clear and consistent data models, improving communication, and enhancing the efficiency of information retrieval and management.
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 framework for discovering and validating the right market for a product, building the right product features, and validating the business model. Important for ensuring that products meet market needs and customer expectations.
A theoretical concept in economics that portrays humans as rational and self-interested agents who aim to maximize their utility. Important for understanding economic decision-making and designing systems that align with rational behavior.
Entity Relationship Diagram (ERD) is a visual representation of the relationships between entities in a database. Essential for designing and understanding the data structure and relationships within digital products.
A statistical technique that uses random sampling and statistical modeling to estimate mathematical functions and simulate systems. Useful for risk assessment, decision-making, and performance optimization in digital product design.
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.
The mathematical study of waiting lines or queues. Useful for optimizing user flow and reducing wait times in user interfaces.
A type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Crucial for developing intelligent systems that can make data-driven decisions.
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.
Monthly Recurring Revenue (MRR) is a metric that quantifies the predictable revenue generated each month from customers. This metric is crucial for SaaS companies to track financial health and growth.
Also known as the 68-95-99.7 Rule, it states that for a normal distribution, nearly all data will fall within three standard deviations of the mean. Important for understanding the distribution of data and making predictions about data behavior in digital product design.
A digital replica of a physical entity, used to simulate, analyze, and optimize real-world operations. Essential for improving operational efficiency and decision-making.
The study of the principles that govern human behavior, including how people respond to stimuli and learn from their environment. Crucial for designing user experiences that anticipate and influence user behavior.
A set of cognitive processes that include working memory, flexible thinking, and self-control, crucial for planning, decision-making, and behavior regulation. Crucial for designing interfaces and experiences that support users' cognitive abilities.
A risk management model that illustrates how multiple layers of defense (like slices of Swiss cheese) can prevent failures, despite each layer having its own weaknesses. Crucial for understanding and mitigating risks in complex systems.
A strategic management template for developing new business models or documenting existing ones, detailing elements like value proposition, infrastructure, and customers. Important for understanding and designing business strategies that align with product and user experience goals.