AI Glossary
As AI shifts from experimentation to enterprise-scale execution, understanding its vocabulary becomes essential. This glossary defines the terms that matter, from core concepts to the evolving language of intelligent systems.
AI Agents are intelligent software entities capable of independently breaking down high-level goals into smaller tasks, using tools or APIs to execute them, and delivering results with minimal human input. They act with intent, adapt to real-time data, and can function across systems to complete complex workflows.
Agentic AI refers to AI systems designed to operate autonomously toward specific goals by combining capabilities like reasoning, planning, memory, and adaptability. These systems can make decisions, break down complex tasks, and act across tools and systems with minimal human input. Whether as a single agent or a network of specialized agents, Agentic AI shifts AI from reactive responses to proactive, goal-oriented problem-solving, making it ideal for dynamic, multi-step workflows.
Term coined by McKinsey, Agentic Mesh refers to a flexible and scalable AI architecture where multiple autonomous agents work together across different tools, systems, and language models. It’s designed to be vendor-agnostic and distributed, allowing agents to collaborate, make decisions, and adapt in real time securely and at enterprise scale.
Agentic Applications are software systems powered by Agentic AI, where autonomous agents can take actions, make decisions, and adapt in real time based on changing inputs. These applications often use technologies like large language models (LLMs), computer vision, and reinforcement learning to handle complex tasks with minimal human guidance.
Agentic Workflows are task sequences planned and carried out by AI agents with minimal human input. These workflows are dynamic, allowing agents to adapt in real time based on context, outcomes, or changing conditions to achieve specific goals efficiently and autonomously.
Autonomous Agents are AI systems that can independently plan, act, and learn to achieve specific goals without constant human direction. They break down tasks, make decisions in real time, adapt to changing conditions, and improve through experience, making them valuable for automating complex, multi-step processes across dynamic environments.
A2A is a communication or coordination mechanism where autonomous AI agents interact directly with one another to delegate tasks, share knowledge, or collaboratively solve problems. It enables distributed decision-making and workflow execution without constant human input, forming the backbone of multi-agent ecosystems.
Agent Platfrom is an enterprise-grade multi-agent orchestration infrastructure for developing, deploying, and managing sophisticated agentic applications at scale. Built on a decade of AI innovation, the platform enables businesses to design and orchestrate AI agents with different levels of autonomy, from guided assistants to fully independent systems, tailored to specific business needs. It’s like giving your enterprise a brain that can think, learn, and act across workflows.
AI for Work is an enterprise productivity AI framework that enhances workforce efficiency by leveraging context-aware AI agents to enterprise knowledge retrieval, automate task execution, and workflow optimization. It enables semantic reasoning, cross-application orchestration, and structured decision intelligence across business functions.
AI for Process is a process intelligence and workflow automation suite that leverages AI-driven process mining, cognitive task modeling, and reinforcement learning to optimize execution paths, manage dynamic exceptions, and enforce compliance-driven process automation. It enables AI agents to autonomously adapt workflow execution based on real-time data and predictive analytics.
AI for Service is a conversational and service automation framework that integrates agentic AI, multi-modal NLP, and real-time adaptive reasoning to handle customer interactions on multiple voice and digital channels in synchronous or asynchronous fashion. It supports intent-driven automation, real-time AI agent augmentation, and hierarchical task delegation, ensuring scalable, omnichannel self-service experiences.
The coordination engine that manages how multiple AI agents work together. It dynamically assigns tasks to the right agent based on the goal, context, or system state, ensuring agents interact smoothly, avoid conflicts, and complete complex workflows efficiently. Think of it as the conductor of an AI orchestra making sure every agent plays its part at the right time.
The reasoning core that breaks down high-level goals into executable steps. It generates multi-step action plans based on user intent, context, and available tools or agents. The planner enables AI agents to act with foresight, deciding what to do, when, and how to fulfill a task autonomously and adapt as conditions change.
Agent Embeddings are vector-based semantic representations that capture an agent’s role, skills, context, or task history. They enable intelligent matchmaking, task routing, and specialization within large pools of agents by helping the system understand which agent is best suited for a specific goal or situation.
Agentic Memory is a structured system that enables AI agents to store, recall, and reason over both short-term and long-term information. Short-term memory captures the immediate context of a task or conversation, while long-term memory retains past interactions, learned knowledge, goals, and decisions. This combined memory allows agents to maintain continuity, personalize responses, make informed decisions, and handle complex workflows without losing context, essentially giving them the ability to learn and adapt over time rather than starting from scratch with each interaction.
Augmentation is the practice of enriching AI models with additional context or knowledge from external sources to improve their performance. Instead of relying only on what the model was trained on, it pulls in real-time data, documents, or tools to produce more accurate, relevant, and grounded outputs. It turns a general-purpose model into a domain-aware, task-specific assistant without retraining.
Agentic RAG blends the power of Retrieval-Augmented Generation with the autonomy of intelligent agents. Unlike traditional RAG, which simply retrieves relevant information and generates a response, Agentic RAG enables agents to actively decide what to retrieve, how to interpret it, and when to act based on the broader goal they’re trying to accomplish. It weaves together retrieval, memory, reasoning, and decision-making, allowing agents to operate in a more context-aware, purposeful, and adaptive manner across multi-step workflows.
Agentic Reasoning is the core capability that allows AI agents to think through problems, make decisions, and adapt on their own. It enables agents to break down complex goals into smaller steps, use context to guide their actions, learn from outcomes, and self-correct along the way. This transforms agents from simple, reactive tools into proactive, goal-oriented problem-solvers that can handle ambiguity, make informed choices, and operate with real autonomy.
AI Analytics refers to the suite of tools and dashboards used to monitor, measure, and improve the performance of AI systems. It captures insights across user interactions, model behavior, intent detection, resolution outcomes, and more. By analyzing this data, businesses can assess accuracy, identify bottlenecks, run A/B tests, fine-tune prompts or workflows, and ensure the AI is aligned with business goals. It’s the intelligence layer that turns raw AI activity into actionable improvement.
Agent Traceability is the ability to track, audit, and visualize how an AI agent makes decisions, including the model calls, tools used, and contextual inputs involved. It provides transparency into agent behavior, supports governance and compliance, and helps identify and resolve errors or unintended actions.
AI Safety is the practice of designing AI systems to operate securely, ethically, and in alignment with human values. It focuses on preventing harmful outcomes like bias, data misuse, adversarial attacks, or unintended actions. This involves building robust, transparent models with strong governance, continuous monitoring, and human oversight to ensure responsible deployment across all stages of AI use.
AI TRiSM, coined by Gartner, stands for Artificial Intelligence Trust, Risk, and Security Management and is a framework designed to ensure AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection throughout the AI lifecycle.
According to Gartner – The act of branding simple AI tools or rule-based bots as “Agentic AI” to ride the hype wave without offering true autonomy, reasoning, or orchestration.
Much like AI-washing, this misleads buyers by slapping the “Agent” label on systems that don’t actually think, plan, or act toward goals. Real agentic AI operates with memory, intent, and adaptability not just scripts and workflows.
AI Simulation is the use of synthetic environments to train and test AI models in controlled, risk-free settings. These simulated worlds allow agents to learn through trial and error, explore complex scenarios, and refine behaviors without real-world consequences, making them ideal for safe experimentation, continuous learning, and performance tuning.
AI Copilot is a smart, context-aware assistant that works alongside you to boost productivity. It offers real-time suggestions, automates repetitive steps, and surfaces relevant insights when you need them most. More than just a passive helper, it collaborates with you to understand your goals, adapts to your workflow, and helps you get things done faster and more efficiently.
An Action Task is a predefined AI-driven task that executes automatically when certain conditions are triggered, like sending a notification, updating a record, or launching a workflow. It’s your system’s way of handling routine actions instantly, so things move forward without manual effort.
An API is a set of rules and protocols that lets your AI system communicate with other software, apps, or databases. It acts like a bridge, allowing data and actions to flow between tools enabling smart, automated workflows without manual intervention.
An Alert Task is an AI-triggered response to anomalies, thresholds, or unexpected events, like flagging suspicious activity, system errors, or performance drops. It instantly notifies the right people or systems, enabling quick action without manual monitoring or investigation.
Auto-NLP is a toolkit that automates key natural language processing tasks like text classification, sentiment analysis, and intent detection with minimal manual setup. It’s ideal for teams who need fast, reliable NLP results without building custom pipelines from scratch.
ASR is the technology that converts spoken words into written text in real time. It’s what powers voice input in apps, IVR systems, and virtual assistants, enabling machines to understand and respond to human speech.
An auto-regressive model generates text by predicting one word or token at a time, using all previous outputs as context. It builds responses step by step, making each prediction based on what it has already generated. GPT models are a common example of this approach.
Agentic X is a paradigm that brings agentic capabilities like reasoning, planning, and autonomy into any application or domain. It enables systems to independently manage complex tasks by breaking them down, adapting on the fly, and coordinating actions without constant supervision.
AI Supercomputing is the high-performance infrastructure that powers today’s most advanced AI systems. These massive compute clusters are built to train and run large language models and generative AI workloads at scale, delivering the speed and capacity needed for complex reasoning, deep learning, and real-time inference across enterprise applications.
Anthropomorphism is the tendency to attribute human traits like emotions, intentions, or consciousness to machines or AI systems. While it can make interactions feel more natural and engaging, it often creates false expectations by blurring the line between what AI appears to do and what it understands.
Artificial Intelligence is a branch of computer science focused on creating machines that can mimic human intelligence, reasoning, learning, decision-making, and problem-solving, often without being explicitly programmed for every task. At its core, it’s automation that can adapt and improve over time.
Artificial General Intelligence is the idea of AI that can understand, learn, and apply knowledge across a wide range of tasks just like a human. Unlike today’s specialized AI systems, AGI would be capable of general reasoning, creativity, and adaptability. It’s still theoretical, but it represents the long-term goal for many in the AI field.
Basic RAG (Retrieval-Augmented Generation) is a method that enhances language models by retrieving relevant information from external sources and using it to generate more accurate, grounded responses. It improves the model’s output by supplementing it with real-time or domain-specific knowledge, rather than relying solely on pre-trained data.
A benchmark is a standardized evaluation used to assess how well an AI model performs on specific tasks, such as reasoning, language understanding, or decision-making. It provides a reliable basis for comparing models, tracking progress, and identifying strengths or weaknesses in real-world applications. As AI systems grow more complex, benchmarks help ensure consistent, transparent measurement across evolving capabilities.
BM25 is a traditional keyword-based retrieval algorithm used to rank documents based on how well they match a search query. It scores documents using term frequency and document length, making it a fast and effective choice for classic information retrieval tasks where exact keyword matching is key.
A Contextual Intelligence Engine is the brain behind an AI system’s understanding of context. It collects and analyzes signals like user roles, conversation history, business rules, and external data to help agents make smarter decisions. Think of it as the layer that gives your AI memory, awareness, and the ability to adapt to what’s happening right now.
Context Engineering is the practice of designing AI systems to understand and use real-world context effectively. This includes things like who the user is, what they’re trying to do, their past interactions, and the surrounding business environment. By engineering how this context is captured, stored, and applied, AI agents can respond more intelligently, personalize their actions, and handle complex workflows with better accuracy.
A Context Router directs requests or tasks to the right agent, model, or workflow based on what’s happening at the moment. It uses contextual signals like what the user said, what system they’re in, or how confident the AI is to make routing decisions automatically. This helps ensure users get the most relevant and accurate help without bouncing around.
Controllability refers to how well you can guide or constrain an AI’s behavior. It ensures the system stays aligned with business rules, compliance standards, or desired tone. This can include setting boundaries on what the AI can say, which tools it can use, or how it responds in risky situations, making AI safer and more predictable in enterprise settings.
Chunking is the process of breaking large documents or datasets into smaller, meaningful pieces called chunks so AI can understand and retrieve them more efficiently. It’s essential for making Retrieval-Augmented Generation (RAG) and enterprise search smarter and faster.
Chain of Thought Prompting is a technique that encourages AI models to “think out loud” before giving a final answer. It guides them to reason through each step like a math problem or troubleshooting task rather than jumping straight to the result. This often leads to more accurate and explainable outputs.
The Context Window is the amount of information a language model can “see” at once when generating a response. Measured in tokens, a larger context window allows the model to consider more history or background, which leads to more coherent, informed, and context-aware outputs. It’s critical for tasks like long conversations, document summarization, or multi-turn reasoning.
Component Reusability means building AI elements like intents, prompts, or connectors so they can be reused across different agents or applications. It speeds up development, maintains consistency, and avoids duplicating effort when scaling AI across teams or departments.
Composable AI is an approach where AI capabilities are built like modular blocks; each component (such as agents, tools, or workflows) can be reused, rearranged, or extended. This enables enterprises to scale AI quickly, customize it for various use cases, and adapt to changes without having to start from scratch.
Contextual Embedding is a method of transforming words, phrases, or data into dense numerical vectors that convey meaning specific to the context in which they’re used. Instead of treating all words the same, it helps AI models understand nuances like the difference between “bank” as a financial institution or a riverbank, based on the surrounding context. This improves how AI retrieves, ranks, and reasons with information.
A Confidence Score tells you how certain the AI is about its prediction or response, like how sure it is that a user wants to reset a password. It’s typically shown as a percentage and helps determine what to do next: proceed, ask for clarification, or escalate to a human.
A Cloud Connector is a plug-and-play integration that links your AI system to third-party cloud apps or services like CRMs, ticketing tools, or databases. It enables seamless data exchange and lets agents take real-time actions without custom code or middleware.
Conversational AI enables machines to interact with humans using natural language via text, voice, or messaging. It powers virtual assistants, chatbots, IVRs, and more, helping automate support, guide users, or execute tasks. At its core, it combines language understanding, intent recognition, and dialogue management to carry out real conversations.
A Conversational UI is a user interface designed for interacting through natural language instead of buttons, forms, or menus. It’s what you see when chatting with a bot in a support window or giving voice commands to a smart assistant. It makes the user experience more intuitive, especially for complex or dynamic tasks.
Cognitive Services are pre-built AI capabilities that handle specific tasks like recognizing speech, analyzing images, translating languages, or detecting emotions. Instead of building models from scratch, teams can plug in these services to give their applications smarter, human-like abilities instantly.
Dialog GPT is an intelligent orchestration engine that powers natural, multi-turn conversations at scale. It autonomously manages the flow from intent detection to task execution, balancing structured business logic with conversational flexibility. By combining embeddings‑based retrieval, generative models, and domain knowledge, it performs zero‑shot intent detection, resolves ambiguity through clarifying questions, and handles multiple intents within a single query without requiring extensive training data
A DSLM is a language model that’s fine-tuned for a specific industry, like healthcare, banking, or telecom. That means it understands the lingo, the context, and what really matters in that space so responses are smarter and more relevant.
A Deliberation Engine gives agents the power to pause and weigh their options before acting. Instead of taking the first available path, it helps them think through alternatives and choose the most effective next move especially useful in complex workflows.
A Dialog Task is like a guided conversation path built to complete a specific job say, scheduling a meeting or checking an order. It connects user inputs to backend systems through logical steps, so the AI can take action, not just chat.
The Dialog Builder is a visual workspace for designing conversations. It lets teams drag, drop, and configure dialog flows without writing much code so you can launch smart, functional bots without needing a developer on every step.
Data Augmentation is a way to expand training data by tweaking or generating new examples. You might rephrase a sentence or add noise to improve robustness. It’s a smart shortcut for making models better without collecting tons of new data.
Before data can be used in AI models, it has to be cleaned up and formatted. Data Preprocessing is that step removing errors, standardizing text, and getting everything into shape so the AI can actually learn something useful from it.
Data Retention is about how long user and system data is stored and when it’s deleted. It’s a key part of staying compliant with privacy laws like GDPR or HIPAA and making sure sensitive info doesn’t linger longer than it should.
Deployment is when your AI system leaves the test lab and goes live. Whether it’s integrated into a chatbot, voice assistant, or internal tool, this is when real users start interacting with the model and when performance really matters.
Dense Retrieval is a smarter way to search. Instead of just matching keywords, it uses vector embeddings to find information that’s semantically similar even if the wording is different. This powers more relevant results in RAG and search systems.
A Deterministic Model is one that always gives the same output for the same input. It’s useful when consistency and traceability matter more than creativity like in legal, financial, or safety-critical workflows.
Enterprise AI is AI built for business, not just for chat. It’s designed to work securely, reliably, and at scale across departments, tools, and users. It handles real business logic, integrates with complex systems, and keeps things compliant and accountable.
Enterprise RAG brings together smart search (retrieval) and smart generation (LLMs), but with enterprise guardrails. It pulls answers from internal sources like knowledge bases or documents, then generates accurate, on-brand responses securely and with full traceability.
Explainable AI is about clarity, showing why the AI made a decision, instead of leaving it a mystery. It’s essential when you need trust, especially in industries like finance, healthcare, or customer support, where “because the AI said so” isn’t good enough.
Embeddings are like digital fingerprints for words or data. They capture the meaning of a phrase, sentence, or document in a way AI can understand so it can find similar content, rank search results, or keep context between steps.
These are the tools that create embeddings, turning everyday language into math that the AI can work with. They help power smarter search, retrieval, and reasoning by making connections based on meaning, not just keywords.
An entity is a specific piece of information the AI is trying to extract like a person’s name, a date, or an account number. Think of it as a key detail that makes a vague request actionable.
This is how the AI pulls useful details out of what someone says or types. For example, if a user says, “I need help with my March invoice,” entity extraction would pull out “March” and “invoice” to help route the task correctly.
Ethical AI means building systems that are fair, respectful, and responsible. It’s about avoiding harmful bias, protecting privacy, and making sure AI is used in ways that align with human values not just business goals.
Encryption keeps data safe by scrambling it so only the right people (or systems) can unlock it. It’s what protects your passwords, personal info, and business data when AI systems are moving things around or storing them.
Edge AI runs right where the data is on devices like phones, kiosks, or local servers rather than in the cloud. It’s fast, private, and great for use cases that need instant decisions or work in places with limited connectivity.
In the AI world, an FAQ usually refers to a set of pre-trained question-answer pairs used by virtual assistants or knowledge bots. It’s the simplest way to give users fast, accurate answers to common queries without needing full conversations or workflows.
Few-shot learning lets a model understand a new task with just a handful of examples. Instead of retraining the whole system, you show it a few samples in the prompt, and it figures things out on the fly. It’s a big deal when speed and flexibility matter.
Foundation Models are large, general-purpose AI models trained on massive datasets. They’re called “foundation” because they can be adapted to many tasks like summarization, answering questions, or classification using techniques like fine-tuning or prompting.
Fine-tuning is the process of taking a big, general AI model and training it on your specific data so it behaves the way you want. It helps align the model with your tone, vocabulary, workflows, or industry-specific needs.
Frontier Models are the most advanced AI systems being developed models that push the boundaries of what AI can do. These are typically massive, multimodal, and capable of reasoning, planning, or even acting autonomously. They’re often still in research or tightly controlled release.
Frontier Models are the most advanced AI systems being developed models that push the boundaries of what AI can do. These are typically massive, multimodal, and capable of reasoning, planning, or even acting autonomously. They’re often still in research or tightly controlled release.
Graph-RAG combines retrieval-augmented generation with knowledge graphs. It doesn’t just pull isolated chunks of information; it understands relationships between data points, improving reasoning, context, and relevance in generated answers.
Grounding means making sure an AI agent’s output is based on trusted information like enterprise documents, databases, or real-time context rather than guesswork. It gives agents a reliable foundation to reason from, so their actions, decisions, and responses are factual, relevant, and safe for enterprise use.
Guardrails Framework puts boundaries around what GenAI can say or do. It helps ensure outputs are safe, appropriate, on-brand, and compliant—whether that means blocking certain phrases, guiding tone, or restricting tool access.
Generative AI refers to AI systems that can create content like text, images, code, or even conversations based on patterns learned from data. Instead of just picking from preset options, it generates new, dynamic output in real time, making interactions feel natural and intelligent.
GPT is a family of powerful generative language models that understand and generate human-like text. They’re “pre-trained” on massive data and can be adapted for everything from chatbots to summarization tools to agents.
Hybrid Search combines keyword-based search with semantic search powered by embeddings. This means the system can retrieve both exact matches and results based on meaning, leading to more relevant, complete answers, especially when queries are open-ended or complex.
A hallucination happens when an AI generates something that sounds right but isn’t true. It can lead to misleading answers or incorrect actions, especially when the AI is handling complex or sensitive tasks. Grounding and validation are key to keeping output accurate and reliable..
Human in the Loop refers to keeping a person involved in the AI decision-making process, either for oversight, approvals, or interventions. It’s a way to balance automation with control, especially in workflows where accuracy, judgment, or compliance matter.
Hyperparameter tuning is the process of fine-tuning settings that control how an AI model learns and performs. It helps improve accuracy, speed, and reliability by optimizing factors like learning rate, model size, or token limits.
In-Context Learning is when an AI model learns how to handle a task on the spot by reading examples in the prompt without being retrained. It’s like giving the model a few demos and having it pick up the pattern instantly, which is especially useful for custom tasks and dynamic use cases.
An intent is what the user wants to do, like “reset my password” or “check my balance.” Detecting the right intent is the first step in helping the AI figure out how to respond, what action to take, or which workflow to trigger.
Ingestion is the process of bringing external data, like documents, FAQs, PDFs, or knowledge base articles, into the AI system. It’s the first step toward making that content searchable, retrievable, and usable in conversations or workflows.
Indexing is the process of organizing and storing data so it can be quickly searched and retrieved by the AI. Whether it’s documents, transcripts, or knowledge articles, indexing makes sure the system knows where to find the right information fast.
Instruction-tuning is a method for training AI models to better follow human instructions. Instead of just predicting what comes next, the model learns to respond in the way users expect whether that’s answering clearly, summarizing concisely, or taking action when asked.
Joint Learning is a training approach where multiple AI tasks or models are learned together instead of separately. By sharing knowledge across tasks, like intent detection and entity recognition, the model can improve overall accuracy and generalization. It’s especially useful in complex systems where different capabilities need to work in sync, like in virtual assistants handling varied user requests.
Knowledge Graphs organize information into connected nodes and relationships, like a map of how concepts, entities, and data points relate to each other. This helps the AI reason more intelligently, so instead of treating facts in isolation, it understands how they connect.
A Knowledge Task is any task where the AI is responsible for finding, understanding, and delivering information, like answering a policy question or summarizing a document. These tasks rely on connecting to the right knowledge source, retrieving relevant content, and presenting it in a helpful way.
A Knowledge Base is a centralized repository of information FAQs, how-to articles, documents, and internal guides that the AI can use to answer questions or support tasks. It’s like the AI’s internal library, helping it respond with consistent, approved information.
A Large Language Model (LLM) is an advanced AI model trained on massive text datasets to understand, process, and generate human language. It can analyze queries, summarize information, complete tasks, and support reasoning across a wide range of language-driven applications. Its strength lies in handling complexity, adapting to different contexts, and delivering coherent, context-aware output at scale.
LLM Orchestration is the process of managing how large language models interact with tools, memory, APIs, and other agents. It ensures the LLM isn’t just generating text, but working as part of a larger system that can reason, retrieve, act, and adapt across workflows.
Long-term memory allows AI agents to remember information across interactions like user preferences, past actions, or previous answers. It helps make responses more personalized, consistent, and goal-aware over time.
Low-code platforms let users build AI-powered applications or automations using visual interfaces instead of traditional coding. It helps business users and non-engineers create workflows, bots, or integrations quickly and safely.
LangOps (Language Operations) is the practice of managing and optimizing how language models are deployed and used across the enterprise. It includes performance tuning, governance, training data management, and model versioning essentially DevOps for LLMs.
LoRA is a technique for fine-tuning large models efficiently, without needing to retrain the whole thing. It makes updates lighter, cheaper, and easier to deploy—perfect for customizing foundation models in enterprise settings.
Multi-agent orchestration is the coordination of multiple specialized AI agents working together to complete complex tasks. Each agent focuses on its part retrieving data, executing actions, or reasoning and the orchestration layer ensures everything flows smoothly.
A multi-agent system is a setup where several autonomous AI agents collaborate, communicate, and share context to solve a broader goal. It’s like a digital team, each agent with its own role working towards the same objective.
Memory allows AI systems to retain and reuse information over time, like past interactions, user preferences, or task history. It helps the AI stay context-aware, make better decisions, and maintain continuity across conversations or workflows.
Multimodal AI refers to systems that can understand and process more than one type of input, like text, images, audio, or video. It enables richer, more flexible interactions across a wider range of tasks and channels.
Multi-vector search improves retrieval by using more than one semantic representation to find relevant information. It helps surface better results by capturing different meanings or perspectives behind a single query.
ModelOps is the practice of managing the full lifecycle of AI models, from training and testing to deployment, monitoring, and retirement. It’s essential for keeping models secure, updated, and aligned with business needs over time.
A Model Router decides which AI model to use for a specific task. Based on factors like prompt type, confidence score, or domain, it directs requests to the best-fitting model ensuring the system stays efficient, accurate, and scalable.
Natural Language Processing is the broad field of AI that helps machines understand, interpret, and work with human language. It covers everything from analyzing text to extracting meaning, enabling systems to handle unstructured input like messages, emails, or voice commands.
NLU is a subset of NLP focused on interpreting the meaning and intent behind what someone says or types. It helps AI systems figure out what the user wants, even if the phrasing is vague or unstructured, critical for driving accurate responses and actions.
NLG is the process of turning structured data or internal knowledge into clear, human-sounding language. Whether it’s summarizing a report or answering a user question, NLG helps AI systems respond naturally and intelligently in real time.
No-code platforms let users build AI applications, workflows, or automations without writing any code. Instead of programming, users work through visual interfaces, like drag-and-drop tools or form-based logic. It makes it possible for business teams to launch and manage AI solutions quickly, without needing deep technical skills.
An ontology is a structured way of organizing knowledge. It defines the relationships between concepts, entities, and categories within a specific domain. In AI systems, it helps the agent understand how things are connected, so it can reason more accurately and respond in a context-aware way.
Omni-channel means providing a seamless AI experience across multiple channels, like chat, voice, email, web, or mobile apps, while keeping context and continuity intact. It ensures that users get consistent support and can pick up where they left off, no matter how or where they interact.
Open-source LLMs are large language models that are freely available for anyone to use, customize, or deploy. They offer flexibility and transparency, making them a strong option for enterprises that want control over model behavior, cost, or deployment environment.
Prompt engineering is the art of crafting instructions that guide an AI model’s behavior. The way a prompt is written can shape the tone, format, and accuracy of the response, making it a powerful tool for improving results without retraining the model.
Prompt chaining is the technique of linking multiple prompts together using the output of one as the input to the next to guide the model through multi-step reasoning or tasks. It helps break down complex problems into manageable steps for more reliable outcomes.
Prompt pipelines are structured sequences of prompts, logic, and decision steps that together drive a larger task. Think of them as reusable flows where each step builds on the last, helping AI systems complete end-to-end actions more reliably.
A pre-trained model is an AI model that’s already been trained on large datasets and can be fine-tuned or used directly for specific tasks. It saves time and resources by offering a solid foundation that can be adapted quickly to new use cases.
Parameters are the internal values that a language model learns during training. They control how the model interprets language, forms associations, and generates responses. In simple terms, more parameters generally mean the model can capture more complexity, but also requires more computation.
A probabilistic model makes decisions or predictions based on the likelihood of different outcomes. Instead of producing one “correct” answer, it weighs possibilities and selects the most likely one, making it useful for language, reasoning, and uncertain scenarios.
Query optimization involves refining a query to make it more efficient, precise, or context-aware so the AI retrieves the best possible answers faster. This could include rephrasing, ranking priorities, or eliminating unnecessary noise in the input before processing it.
RAG is an AI technique that brings together three components, retrieval, augmentation, and generation, to produce more accurate and context-aware responses. First, it retrieves relevant information from trusted external sources, such as documents or databases. Then it augments the input by combining the user’s query with the retrieved data, giving the model richer context to work with. Finally, it generates a response based on that combined input. This approach reduces hallucinations, keeps answers grounded in real knowledge, and makes the system more reliable, especially for enterprise use cases where accuracy and traceability are essential.
Reasoning is the AI’s ability to think through a problem, break it into steps, and make informed decisions. It’s what separates reactive bots from intelligent agents that can handle ambiguity, follow goals, and adapt in real time.
Responsible AI means building and deploying AI systems that are ethical, transparent, fair, and aligned with human values. It covers things like avoiding bias, respecting privacy, and making sure decisions can be explained and trusted.
RBAC restricts access to features or data based on a user’s role, like admin, agent, or end user. It’s essential in enterprise AI platforms for protecting sensitive information and enforcing security policies across teams.
Reinforcement Learning is a method where AI learns by trial and error, getting rewarded for good outcomes and penalized for bad ones. It’s useful for training agents to improve over time in dynamic or goal-driven environments.
RLHF combines reinforcement learning with human guidance. Instead of just learning from rules, the AI improves by watching how humans rate or correct its outputs, leading to responses that better match expectations and values.
RPA automates repetitive tasks using bots that mimic human actions, like clicking buttons or copying data between systems. While powerful for rule-based tasks, it lacks the reasoning and flexibility of agentic AI, which can adapt to changing goals and context.
Small Language Models are compact AI models trained for specific tasks or domains. They’re faster, more cost-effective, and easier to control than massive models, making them ideal for use cases that require speed, privacy, or domain precision
Search and Data AI is Kore.ai’s intelligent framework for enterprise knowledge discovery. It brings together agentic RAG, semantic understanding, multi-source connectors, and hybrid vector search to turn scattered internal data, like documents, databases, FAQs, or web content, into accurate, context-rich answers. The system intelligently ingests and indexes information, applies semantic and keyword searches, and wraps it all in conversational AI that can ask follow‑ups, maintain context across the session, and smoothly switch to a live agent when needed.
Semantic search goes beyond keywords to understand the meaning behind a query. It helps AI systems find relevant content, even if the wording doesn’t exactly match, by looking at intent, context, and relationships between concepts.
Short-term memory stores recent inputs, decisions, or conversational context that an agent uses during an active session. It helps the system stay coherent and relevant within a task, without mixing it up with long-term data or unrelated past interactions.
Supervised learning is when an AI model is trained using labeled data, examples where the input and correct output are known. It’s widely used for tasks like classification, prediction, and intent recognition.
An SDK is a collection of tools, libraries, and documentation that helps developers build or extend AI applications. It provides everything needed to integrate with APIs, build custom features, or embed AI into enterprise workflows.
Sentiment analysis helps AI understand emotions behind text, whether it’s positive, negative, or neutral. It’s useful in support, marketing, and feedback systems to assess customer tone and urgency.
Sparse retrieval relies on traditional keyword matching methods to retrieve content. It’s fast and effective for exact matches, but often less flexible than semantic search when queries are vague or varied.
Scaffolding is a technique where a complex task is broken into smaller steps that the AI can reason through, often using intermediate prompts, models, or agents. It’s helpful for multi-step reasoning, planning, and decision-making.
Sequence modeling is the process of analyzing or predicting patterns in ordered data, like sentences, clickstreams, or time-series events. It’s essential for tasks where the order of information affects the outcome, such as language processing or behavior prediction.
Synthetic data generation involves creating artificial data, like text, images, or records, to train or test AI models. It’s especially useful when real data is limited, sensitive, or needs to be balanced for fairness.
Training data is what an AI system learns from. It could be text, documents, conversations, anything that teaches the model how language works and what to expect. The better the training data, the smarter and more accurate the AI becomes.
The transformer is a type of AI model architecture that made today’s powerful language models possible. It helps the AI understand how words relate to each other in a sentence so it can generate responses that make sense.
Tokens are the chunks of text that an AI model reads or writes, like words or parts of words. The more tokens you give the model, the more context it has to work with. But there’s always a limit, so choosing what goes in really matters.
Transparency means you can see and understand how an AI system came to its answers. It helps build trust, especially in business settings where decisions need to be explained, tracked, and improved over time.
Temperature is a setting that controls how “creative” the AI gets. A low temperature keeps responses focused and predictable. A higher one makes answers more diverse, but sometimes less accurate. It’s like adjusting how bold the AI is allowed to be.
Testing is how we make sure AI works as expected before it goes live. It includes checking accuracy, behavior, and edge cases, so there are no surprises when customers or teams start using it.
A taxonomy is just a fancy word for a well-organized list of categories. It helps the AI organize things in a meaningful way, like types of customer issues, product categories, or departments, so it knows how to respond and route information correctly.
Transfer learning is when an AI takes what it learned from one task and uses it for another. It’s like reusing knowledge, saving time, effort, and making the model smarter, faster.
Toxicity is when AI says something harmful, offensive, or inappropriate. It’s not always intentional, it’s just repeating patterns it has seen. That’s why filters and safeguards are used to catch and prevent it from showing up in responses.
Unstructured data is information that doesn’t follow a fixed format, like emails, chat logs, PDFs, images, or audio files. It’s messy but rich with insights, and AI systems are designed to make sense of it by extracting meaning, context, and intent.
Unsupervised learning is when AI is trained on data without labels. It learns by spotting patterns, clusters, or relationships on its own, which is useful for organizing data or discovering hidden insights without manual setup.
Vector search finds information based on meaning, not just keywords. It compares “embeddings”,numerical representations of content, to return the most relevant results, even when the user’s words don’t exactly match the document.
A vector database is where those embeddings are stored and searched. Instead of matching text directly, it compares how similar the ideas behind different pieces of content are making search more accurate, especially for open-ended queries.
Zero-shot learning lets an AI handle tasks it hasn’t been explicitly trained on just by understanding the instructions. It’s like giving the model a prompt and having it figure things out without needing examples or retraining.
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