Agentic AIAgentic AI refers to a class of artificial intelligence systems designed to autonomously perceive their environment, reason through complex problems, and execute multi-step actions to achieve a specific goal Read More
AI Agents for EnterprisesAI Agents for Enterprises are advanced software systems designed to perform autonomous tasks within a business environment. Unlike passive AI tools that wait for a prompt, AI Agents are goal-oriented: they perceive their environment, reason through complex problems, and use enterprise tools (like CRM, ERP, or HRIS) to execute workflows from start to finish. Read More
AI AutomationAI Automation (often used interchangeably with Intelligent Automation) is the use of artificial intelligence technologies such as Machine Learning, [Computer Vision], and Natural Language Processing to automate tasks that previously required human cognitive abilities. Read More
AI PluginAn AI Plugin is a modular software extension that enables a Large Language Model (LLM) or AI agent to interact with external data sources, third-party software, and real-time web services. While a base AI model is "frozen in time" (limited to the data it was trained on), a plugin acts as a bridge, allowing the AI to "step out" of its training set to perform live actions such as booking a flight, searching current stock prices, or editing a file in your Dropbox.
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AI Service DeskAn AI Service Desk (often called an Automated Service Desk or Virtual Support Agent) is an intelligent software platform that resolves employee support requests autonomously using artificial intelligence. Read More
AI Workflow AutomationAI Workflow Automation involves the use of artificial intelligence technologies specifically Agentic AI, Machine Learning, and Natural Language Processing to orchestrate complex business processes end-to-end without constant human oversight Read More
Automated Employee SupportAutomated Employee Support is the use of intelligent technology such as Agentic AI, chatbots, and workflow automation to resolve internal employee inquiries and service requests without the need for human intervention. Read More
BenchmarkingBenchmarking is the systematic process of measuring an organization's internal processes, products, and performance metrics against those of industry leaders or direct competitors to identify gaps and opportunities for improvement. Read More
Bias in AIBias in AI (also known as Machine Learning Bias or Algorithmic Bias) refers to systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. Read More
BOT VS. AGENTA Bot is a software application programmed to perform a specific, repetitive task based on a rigid set of rules or a script. It waits for a specific trigger and executes a pre-defined action. Read More
Bounded AutonomyBounded Autonomy is a governance framework for artificial intelligence that grants an AI agent the freedom to make decisions and execute tasks independently, but only within a specific, pre-defined set of constraints (the "bounds"). Read More
Built-in GuardrailsBuilt-in Guardrails are the safety mechanisms, filters, and control layers integrated directly into an Artificial Intelligence platform or Large Language Model (LLM) architecture. Their purpose is to detect and block harmful, inaccurate, or non-compliant content before it reaches the user.
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Business Process AutomationBusiness Process Automation (BPA) is the use of technology to execute recurring tasks or processes in a business where manual effort can be replaced. It is designed to route information, tasks, and documents between people and systems based on defined business rules. Read More
Business Rule EngineA Business Rule Engine (BRE) is a software system that executes decision logic independently from the core application code. It enables non-technical business users to define, test, and manage complex rules (logic like "If Customer is Gold Tier, give 10% discount") without relying on IT developers to write or modify software code. Read More
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Chat BotA Chat Bot (or Chatbot) is a software application designed to simulate human conversation with users via text or voice commands. It acts as a conversational interface between a human and a machine, allowing users to ask questions, perform tasks, or retrieve information without navigating complex websites or waiting for a human agent. Read More
Closed-Loop AutomationClosed-Loop Automation is a system that continuously monitors a business process or IT environment, detects deviations from the desired state, and automatically executes corrective actions to restore stability without human intervention.
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Cognitive ArchitectureCognitive Architecture is a blueprint for designing intelligent systems that models the structures and processes of the human mind. It attempts to create a unified framework where an Artificial Intelligence can not just perform isolated tasks (like recognizing a cat), but actually "think" integrating perception, memory, learning, and decision-making into a cohesive whole.
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Cognitive AutomationCognitive Automation is the advanced subset of artificial intelligence that simulates human thought processes to automate complex, non-routine tasks. It combines technologies like [Machine Learning], [Natural Language Processing (NLP)], and data mining to bring intelligence to information-intensive processes. Read More
Compliance-Aware AICompliance-Aware AI refers to artificial intelligence systems specifically architected to understand, monitor, and enforce legal and regulatory standards in real-time. Unlike standard AI, which optimizes purely for speed or accuracy, Compliance-Aware AI optimizes for adherence to rules. Read More
Contextual UnderstandingContextual Understanding is the ability of an Artificial Intelligence system to retain information from previous interactions, environmental cues, and user history to interpret the meaning of a current input. It transforms a disjointed series of questions into a coherent, flowing conversation.
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Continuous LearningContinuous Learning (also known as Lifelong Learning or Incremental Learning) is the capability of an Artificial Intelligence system to learn from new data streams continuously, improving its knowledge and accuracy over time without forgetting what it previously learned. Read More
ControllabilityControllability is the measure of how effectively a human or external system can influence, guide, or override the behavior of an Artificial Intelligence model. It refers to the capacity to force the AI to adhere to specific constraints, styles, or logic paths, rather than letting the model behave randomly or unpredictably. Read More
Conversational AIConversational AI is a set of technologies that enables computers to simulate real human conversation. It bridges the gap between human language (which is messy and complex) and computer language (which is binary and rigid), allowing users to interact with devices using text or speech just as they would with a person.
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Conversational TicketingConversational Ticketing is the integration of enterprise service desk functionality directly into corporate messaging platforms like Slack, Microsoft Teams, or WhatsApp. It allows employees to create, view, manage, and approve support tickets completely within the chat interface, without ever logging into a separate web portal. Read More
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Data AugmentationData Augmentation is a strategy used in Machine Learning to artificially increase the diversity and size of a training dataset without collecting new data. It works by taking existing data points (like an image or a sentence) and applying random transformations such as flipping, rotating, adding noise, or swapping synonyms to create "new" versions of the same data. Read More
ExtractionExtraction is the automated process of identifying and retrieving specific data points from unstructured or semi-structured sources such as PDFs, emails, handwritten forms, or websites—and converting them into a structured format (like a database or spreadsheet). Read More
Data Privacy in AIData Privacy in AI refers to the techniques and governance frameworks used to protect sensitive information (PII, PHI, Trade Secrets) throughout the lifecycle of an artificial intelligence system from training data collection to model deployment Read More
Decision IntelligenceDecision Intelligence (DI) is a discipline that combines data science, social science, and managerial science to model, align, execute, and monitor decision-making processes. It uses AI not just to show you data, but to recommend specific actions and predict their outcomes Read More
Deep LearningDeep Learning (DL) is a specialized subset of Machine Learning inspired by the structure of the human brain. It uses multi-layered artificial neural networks to solve complex problems by progressively extracting higher-level features from raw input.
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Deterministic GuardrailsDeterministic Guardrails are security controls for Artificial Intelligence that rely on explicit, hard-coded rules to validate inputs and outputs. Unlike "Probabilistic Guardrails" (which ask an LLM to "please check if this is safe"), deterministic guardrails do not use AI to judge AI. They use code. Read More
Deterministic ModelA Deterministic Model is a mathematical or computational system where the outcome is precisely determined through known relationships among states and events, without any room for random variation. In this model, if you start with the exact same initial conditions and inputs, you will always get the exact same result, 100% of the time. Read More
Digital EmployeeA Digital Employee (sometimes called a Digital Worker) is a sophisticated software bot powered by Artificial Intelligence that is designed to perform a specific job function, much like a human employee. Unlike a simple script that just "moves data," a Digital Employee has a persona, a role (e.g., "IT Service Desk Agent"), and a set of skills that allow it to converse, reason, and execute complex workflows. Read More
Domain-Specific AIDomain-Specific AI (also known as Vertical AI) refers to artificial intelligence models that are trained or fine-tuned exclusively on datasets from a single industry or field of knowledge, such as healthcare, legal, finance, or coding. Read More
Dynamic Workflow AutomationDynamic Workflow Automation is a technology that allows business processes to change their path in real-time based on data, context, or user behavior. Unlike traditional "Linear Automation," which follows a strict Step 1 → Step 2 → Step 3 sequence, Dynamic Workflows are non-linear. They can skip steps, loop back, request extra information, or branch into entirely new sub-processes depending on what happens during execution. Read More
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Enterprise AIEnterprise AI refers to the specialized application of artificial intelligence (including Machine Learning, NLP, and Computer Vision) to large-scale business operations. Unlike consumer-grade AI (like a free chat bot), Enterprise AI is built to meet strict corporate standards for data privacy, security, regulatory compliance, and high-volume performance.
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Enterprise GraphAn Enterprise Graph is a unified data structure that represents an organization's entire knowledge domain as a network of interconnected entities (nodes) and their relationships (edges). Read More
ExplainabilityExplainability (or Explainable AI / XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. It answers the critical question: "Why did the AI make this specific decision?" Read More
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Federated SearchFederated Search is a search architecture that allows a user to query multiple disparate data sources such as databases, cloud storage, APIs, and document repositories—simultaneously through a single search interface. Read More
Few-Shot LearningFew-Shot Learning (FSL) is a machine learning approach where a model is designed to recognize and generalize to new tasks after seeing only a very small number of training examples (typically between 1 and 5). Read More
Fine-TuningFine-Tuning is the process of taking a pre-trained "Foundation Model" (which has already learned general language, patterns, or logic from a massive dataset) and performing additional training on a smaller, specialized dataset. This secondary training phase adjusts the model's internal weights to make it an expert in a specific niche, such as legal terminology, medical coding, or a company's internal brand voice. Read More
Foundation ModelA Foundation Model (FM) is a large-scale Artificial Intelligence model trained on a vast and diverse amount of data (usually through self-supervised learning) that can be adapted to a wide range of downstream tasks. Read More
Generative AIGenerative AI is a branch of artificial intelligence focused on creating entirely new content including text, images, video, audio, and software code rather than simply analyzing or classifying existing data. It works by using complex neural networks to learn the underlying patterns and structures of a training dataset and then synthesizing new outputs that are statistically similar to the original Read More
Governed Self-ServiceGoverned Self-Service is an operational model in data analytics and AI that provides non-technical business users with the tools to access, analyze, and visualize data independently, but within a strictly defined framework of "Guardrails" set by IT Read More
GroundingGrounding is the process of connecting an Artificial Intelligence model to a specific, reliable source of "truth" such as a company’s private database, real-time web search, or a set of uploaded documents. Without grounding, an AI relies solely on its internal training data, which might be outdated, incomplete, or result in "hallucinations" (confident but false answers). Read More
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HallucinationIn Artificial Intelligence, a hallucination occurs when a generative model such as an LLM or image generator produces an output that is factually incorrect, nonsensical, or disconnected from reality, yet presents it with high confidence and logical coherence. Read More
Human-Agent HandoffHuman-Agent Handoff is the specific mechanism within an automated workflow where an AI Agent determines it can no longer complete a task autonomously and transfers control to a human operator. This transition ensures that complex, high-stakes, or emotionally sensitive issues are handled by people, while the AI manages the routine "heavy lifting." Read More
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Instruction-TuningInstruction-Tuning (often called Supervised Fine-Tuning or SFT) is a machine learning technique used to further train a pre-trained Large Language Model (LLM) on a dataset of (Instruction, Output) pairs. While a base model is only trained to predict the "next most likely word" in a sequence, Instruction-Tuning specifically teaches the model how to act as a responsive assistant that can follow human commands.
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Intelligence AmplificationIntelligence Amplification (IA) also referred to as Cognitive Augmentation or Machine-Augmented Intelligence is the use of information technology to enhance or "amplify" human intelligence. Unlike Artificial Intelligence, which aims to create an autonomous machine that acts as an independent "brain," IA focuses on the Human-in-the-Loop (HITL) model. Read More
Intent DiscoveryIntent Discovery is the automated process of analyzing historical conversation logs from chatbots, call center transcripts, or emails to identify new, previously unknown user goals or patterns of behavior. Read More
Inter-System OrchestrationInter-System Orchestration is the high-level coordination and management of automated workflows that span across multiple independent platforms, applications, and infrastructure environments. Unlike basic automation (which handles single tasks) or intra-system orchestration (which manages tasks within one app), Inter-System Orchestration acts as the "General Contractor" for the entire digital ecosystem.
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InterpretabilityInterpretability refers to the degree to which a human can observe the internal mechanics of an AI model and understand exactly how it arrives at a decision. It is a fundamental property of the model’s architecture. Read More
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Just-in-Time KnowledgeJust-in-Time Knowledge (JITK) is a delivery model where information, training, or insights are provided to a user at the exact moment they need them to complete a specific task. Borrowing from the "Just-in-Time" manufacturing philosophy, JITK rejects the idea of front-loading vast amounts of "Just-in-Case" training that might be forgotten before it is ever applied Read More
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K-Shot LearningK-Shot Learning is a specific paradigm within machine learning where a model is trained or evaluated on its ability to generalize to a new task given exactly $k$ labeled examples per class. In this context, $k$ (the "shot") represents the number of training samples provided to the model to help it recognize a new category.
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Knowledge GenerationKnowledge Generation is the process by which an organization creates new, actionable insights, theories, or solutions by synthesizing existing information, data, and human expertise. Unlike Knowledge Retrieval, which simply finds a "lost" document, Knowledge Generation produces something that did not exist before such as a new product strategy, a scientific hypothesis, or a predictive market trend.
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Knowledge OrchestrationKnowledge Orchestration is the high-level coordination and management of an organization's collective intelligence both structured (databases) and unstructured (documents, chats, emails) to ensure it is delivered to the right person or AI agent at the exact moment of need Read More
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Large Language ModelA Large Language Model (LLM) is a type of Artificial Intelligence trained on vast datasets of trillions of words from books, websites, and code to understand, summarize, generate, and predict new content. At their core, LLMs are massive neural networks based on the Transformer Architecture. Read More
LatencyLatency is the measurement of time delay between a cause and an effect within a system. In computing and telecommunications, it represents the "wait time" (usually measured in milliseconds, $ms$) for a data packet to travel from its source to its destination or for a system to respond to a specific request Read More
Low-CodeLow-Code is a software development approach that enables the rapid creation of applications by using visual, drag-and-drop interfaces instead of traditional, line-by-line manual programming Read More
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Machine Learning (ML)Machine Learning (ML) is a subfield of Artificial Intelligence (AI) focused on building systems that can learn from data, identify patterns, and make decisions with minimal human intervention. Unlike traditional software, which relies on "hard-coded" rules (e.g., if X happens, then do Y), ML uses mathematical algorithms to create a model that improves its performance as it is exposed to more data Read More
Model ChainingModel Chaining is an architectural pattern in which multiple AI models are linked together in a sequence, such that the output of one model serves as the input for the next. This approach allows developers to break down a high-complexity problem into smaller, specialized sub-tasks, each handled by the model best suited for that specific job. Read More
Multi-hop ReasoningMulti-hop Reasoning is the cognitive process where an AI system connects multiple, distinct pieces of information often from different documents or data sources to arrive at a conclusion. Read More
Multi-Turn ConversationA Multi-Turn Conversation is an interaction between a human and an AI system that spans multiple back-and-forth exchanges (or "turns") rather than ending after a single prompt and response Read More
Multimodal Language ModelA Multimodal Language Model (MMLM) is an advanced AI system capable of processing, understanding, and generating information across multiple "modalities" or types of data, such as text, images, audio, and video. Read More
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N-Shot LearningN-Shot Learning is a machine learning paradigm where a model is trained or evaluated on its ability to recognize new concepts or perform new tasks given only $n$ labeled examples. The variable $n$ (the "shot") represents the number of training samples provided for each category the model must learn. Read More
Natural Language Ambiguity(NLA)Natural Language Ambiguity (NLA) is a fundamental characteristic of human communication where a single word, phrase, or sentence can be interpreted in more than one way. While the human brain resolves most ambiguities instantly using common sense and context, it remains one of the most significant challenges for Artificial Intelligence Read More
Natural Language Generation(NLG)Natural Language Generation (NLG) is a subfield of Artificial Intelligence that focuses on the autonomous creation of human-like text or speech from non-linguistic data. While NLU acts as the "ears" (understanding what is said), NLG acts as the "Mouth" of the AI Read More
Natural Language Processing (NLP)Natural Language Processing (NLP) is a multidisciplinary field of Artificial Intelligence (AI) that focuses on the interaction between computers and human language. Its primary goal is to enable machines to read, decipher, understand, and make sense of human languages in a way that is valuable Read More
Natural Language Understanding (NLU)Natural Language Understanding (NLU) is a specialized subfield of Artificial Intelligence focused on enabling computers to interpret the meaning, intent, and sentiment behind human language. Read More
Neural NetworkA Neural Network (also called an Artificial Neural Network or ANN) is a computational model inspired by the biological structure and functioning of the human brain. It consists of interconnected layers of "neurons" (nodes) that process information through mathematical weightings Read More
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OptimizationOptimization is the mathematical and algorithmic process of making an AI model as effective as possible by minimizing its errors and maximizing its performance. In the context of AI, optimization usually refers to the search for the "best" set of internal parameters (weights and biases) that allow a model to accurately predict outcomes or generate content. Read More
Out-of-the-Box (OOTB) SkillsOut-of-the-Box (OOTB) Skills refer to the pre-configured, modular capabilities that an AI platform or autonomous agent possesses immediately upon deployment. These skills are "off-the-shelf" solutions designed to handle common business tasks such as summarizing documents, routing IT tickets, or analyzing sentiment without requiring the customer to write a single line of code or train a custom model. Read More
OverfittingOverfitting is a modeling error that occurs when a machine learning model learns the training data "too well." Instead of identifying the broad, underlying patterns that apply to all data, the model begins to memorize the specific "noise," random fluctuations, and outliers within the training set.
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Pre-trainingPre-training is the foundational stage of developing a machine learning model, particularly for Large Language Models (LLMs) and Computer Vision. In this phase, an AI model is exposed to a massive, unlabeled dataset (often trillions of words or images) to learn the underlying structure, grammar, logic, and "world knowledge" of the data. Read More
Probabilistic ModelA Probabilistic Model is a mathematical representation that incorporates random variables and probability distributions to predict the likelihood of various outcomes. Unlike traditional "if-then" logic, which is rigid and binary, probabilistic models embrace uncertainty Read More
Prompt EngineeringPrompt Engineering is the strategic process of designing, refining, and optimizing inputs (prompts) to guide Large Language Models (LLMs) toward generating the most accurate, relevant, and high-quality outputs possible. Rather than writing code to tell a computer how to calculate a result, prompt engineering uses natural language to tell a model what the desired outcome should be. Read More
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ReasoningReasoning is the high-level cognitive process that enables an AI system to evaluate facts, apply logic, and draw conclusions that were not explicitly stated in its training data. While basic AI is excellent at "pattern matching" (recognizing a cat in a photo), Reasoning AI is capable of "logical inference" (solving a math word problem or debugging complex software). Read More
Recursive PromptingRecursive Prompting is an advanced AI orchestration technique where the output of a prompt is fed back into the model as a new prompt, or used to trigger a sub-task that informs the original goal. Read More
Reinforcement LearningReinforcement Learning (RL) is a branch of machine learning where an autonomous "agent" learns to make decisions by performing actions within an environment to achieve a specific goal. Unlike supervised learning, which relies on a teacher providing the "correct" answers, RL is based on Trial and Error. Read More
Responsible AIResponsible AI is a governance framework and a set of design principles aimed at ensuring that AI systems are developed and deployed in a manner that is ethical, transparent, fair, and safe. It is not just a technical feature but a holistic approach that balances technological innovation with human values and legal compliance. Read More
Sequence ModelingSequence Modeling is a specialized branch of machine learning designed to process, interpret, and predict data where the order of elements is the most critical feature. Unlike standard models that treat data points as independent (e.g., a single image of a dog), sequence models understand that the meaning of a data point depends on what came before it and what follows it.
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Speech-to-TextSpeech-to-Text (STT), also known as Automatic Speech Recognition (ASR), is a technology that uses specialized AI models to transcribe spoken language into digital text. Unlike early versions that relied on rigid phonetic dictionaries, modern STT in 2026 uses deep neural networks, specifically Transformer Architectures to understand patterns in human speech, including varying accents, dialects, and environmental noise. Read More
Stable DiffusionStable Diffusion is an open-source, deep learning text-to-image model released by Stability AI. It belongs to a class of generative AI called Latent Diffusion Models (LDM). Unlike other models that process images pixel-by-pixel, Stable Diffusion operates in a "Latent Space" a compressed mathematical representation of an image which allows it to generate high-resolution visuals using significantly less computing power. Read More
StackingStacking, formally known as Stacked Generalization, is an ensemble learning technique that combines multiple machine learning models (called "base models" or "level-0 models") by using a separate model (called a "meta-model" or "level-1 model") to intelligently blend their predictions. Read More
SteerabilitySteerability refers to the capability of an AI model to adapt its behavior, tone, style, and constraints in real-time based on specific user guidance or external inputs. Unlike Fine-Tuning, which permanently changes a model’s "brain," steerability focuses on "nudging" the model during the generation process Read More
Stochastic ParrotThe term Stochastic Parrot is a metaphor used to describe Large Language Models (LLMs) that are capable of generating highly plausible, human-like text by predicting the next most likely word in a sequence, but which do not actually "understand" the concepts, logic, or reality behind those words Read More
Strong AIStrong AI, often used interchangeably with Artificial General Intelligence (AGI), refers to a theoretical form of artificial intelligence that possesses the ability to understand, learn, and apply its intelligence to any intellectual task that a human being can. Read More
Structured DataStructured Data refers to information that has been organized into a highly formatted and predictable model, typically in the form of rows and columns. This data is governed by a predefined schema (a set of rules), ensuring that every piece of information fits into a specific category such as a date, a currency, or a zip code Read More
SummarizationSummarization is the process of using Artificial Intelligence to condense large volumes of data including text, audio, and video into a shorter, coherent version that retains the core meaning, key themes, and actionable insights. Read More
Supervised LearningSupervised Learning is the most common paradigm of machine learning, where an AI model is trained on a "labeled" dataset. In this setup, the algorithm is provided with input-output pairs think of it as a student being given a set of practice problems along with the answer key. Read More
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