Site Reliability Engineering (SRE) is a discipline that incorporates aspects of software engineering and applies them to infrastructure and operations problems to create scalable and highly reliable software systems. Crucial for maintaining the reliability and efficiency of complex software systems.
A research design where the same participants are used in all conditions of an experiment, allowing for the comparison of different conditions within the same group. Essential for reducing variability and improving the reliability of experimental results.
The ability of a system to maintain its state and data across sessions, ensuring continuity and consistency in user experience. Crucial for designing reliable and user-friendly systems that retain data and settings across interactions.
The process of running a system for an extended period to detect early failures and ensure reliability. Important for ensuring the stability and performance of digital products before full-scale deployment.
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.
The process of maintaining, updating, and improving a product or system after its initial deployment to ensure its continued functionality, performance, and relevance to users. Crucial for ensuring long-term user satisfaction, product reliability, and adaptation to changing user needs and technological advancements.
An experimental design where subjects are paired based on certain characteristics, and then one is assigned to the treatment and the other to the control group. Important for reducing variability and improving the accuracy of experimental results.
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.
The distribution of a new or updated software product to users. Important for delivering new features, improvements, and fixes to users, ensuring continuous enhancement of the product.
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 bias that occurs when researchers' expectations influence the outcome of a study. Crucial for designing research methods that ensure objectivity and reliability.
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.
An environment used for testing software to identify issues and ensure quality before production deployment. Important for detecting and fixing bugs to ensure the software's reliability and performance.
The process of designing, developing, and managing tools and techniques for measuring performance and collecting data. Essential for monitoring and improving system performance and user experience.
Data points that differ significantly from other observations and may indicate variability in a measurement, experimental errors, or novelty. Crucial for identifying anomalies and ensuring the accuracy and reliability of data in digital product design.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
A testing methodology that verifies the complete workflow of an application from start to finish, ensuring all components work together as expected. Important for ensuring the reliability and performance of digital products, leading to better user satisfaction and fewer post-launch issues.
A Japanese word meaning inconsistency or variability in processes. Helps in recognizing and addressing workflow imbalances to improve efficiency.
The hardware and software environment used to deploy and manage applications and services. Essential for ensuring reliable and scalable application deployment.
CSAT (Customer Satisfaction) measures how products or services provided by a company meet or exceed customer expectations. Essential for understanding customer needs and improving product offerings.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
Business Process Automation (BPA) refers to the use of technology to automate complex business processes. Essential for streamlining operations, reducing manual effort, and increasing efficiency in recurring tasks.
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.
Application Support Engineer (ASE) is a professional responsible for maintaining and supporting software applications, ensuring their availability and performance. Crucial for ensuring the reliability and user satisfaction of digital products through effective support and maintenance.
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
A practice of performing testing activities earlier in the software development lifecycle to identify and address issues sooner. Essential for improving software quality, reducing defects, and accelerating development cycles in digital product design.
Garbage In-Garbage Out (GIGO) is a principle stating that the quality of output is determined by the quality of the input, especially in computing and data processing. Crucial for ensuring accurate and reliable data inputs in design and decision-making processes.
Application Release Automation (ARA) is the process of automating the release of applications, ensuring consistency and reducing errors. Crucial for accelerating the delivery of software updates and maintaining high-quality digital products.
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
A software development practice where code changes are automatically prepared for a release to production. Crucial for ensuring rapid and reliable deployment of updates.
A cognitive bias where people see patterns in random data. Important for designers to improve data interpretation and avoid false conclusions based on perceived random patterns.
The use of software tools to run tests on code automatically, ensuring functionality and identifying defects without manual intervention. Crucial for maintaining high code quality and efficiency in the development process.
A set of fundamental principles and guidelines that inform and shape user research practices. Crucial for maintaining consistency and ensuring high-quality user insights.
A software development practice where code changes are frequently integrated into a shared repository, with each change being verified by automated tests. Essential for catching errors early and improving the quality of software.
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 range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter. Essential for making inferences about population parameters and understanding the precision of estimates in product design analysis.
A tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
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.
A cognitive bias where people ignore general statistical information in favor of specific information. Critical for designers to use general statistical information to improve decision-making accuracy and avoid bias.
The process of identifying unusual patterns or outliers in data that do not conform to expected behavior. Crucial for detecting fraud, errors, or other significant deviations in various contexts.
A set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle and deliver high-quality software continuously. Crucial for improving the speed, efficiency, and quality of software development and deployment.
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 software development practice where code changes are automatically deployed to production without manual intervention. Important for maintaining a high level of productivity and quality in software development.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
A deployment strategy where a new version is released to a small subset of users to detect any issues before a full rollout. Crucial for minimizing risk and ensuring the stability of digital products during updates and deployments.
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.
A server dedicated to automating the process of building and compiling code, running tests, and generating software artifacts. Crucial for ensuring continuous integration and maintaining the integrity of the codebase in digital product development.
The process of overseeing and coordinating the development, testing, and deployment of software releases to ensure they are delivered efficiently and effectively. Essential for managing software development cycles and ensuring successful product releases.
A structure or framework used to create effective prompts for AI systems, ensuring clarity and context. Important for standardizing and improving AI prompt design.
A cognitive bias where people ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.
A testing method that examines the internal structure, design, and coding of a software application to verify its functionality. Essential for ensuring the correctness and efficiency of the code in digital product development.
Numeronym for the word "Observability" (O + 11 letters + N), the ability to observe the internal states of a system based on its external outputs, facilitating troubleshooting and performance optimization. Crucial for monitoring and understanding system performance and behavior.
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.
A non-production environment used for development and testing before deployment to production. Important for ensuring that changes are thoroughly tested before going live.
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 statistical phenomenon where a large number of hypotheses are tested, increasing the chance of a rare event being observed. Crucial for understanding and avoiding false positives in data analysis.
Internet of Things (IoT) refers to a network of interconnected physical devices embedded with electronics, software, sensors, and network connectivity, enabling them to collect and exchange data. Essential for creating smart, responsive environments and improving efficiency across various industries by enabling real-time monitoring, analysis, and automation.
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
A testing method that examines the code, documentation, and requirements without executing the program. Important for identifying defects early in the development lifecycle, improving the quality and reducing the cost of digital products.
A principle that states tasks always take longer than expected, even when considering Hofstadter's Law itself. Important for setting realistic project timelines and managing expectations in digital product development.