AI terminology explained in plain English. From foundational concepts to advanced techniques—understand the language of AI transformation.
An autonomous software program that uses artificial intelligence to perform tasks, make decisions, and interact with users or systems. AI agents can range from simple chatbots to complex Super Agents capable of multi-step reasoning and workflow automation.
A set of protocols that allows different software applications to communicate with each other. In AI, APIs enable businesses to integrate AI capabilities into their existing systems without building from scratch.
The use of technology to perform tasks with minimal human intervention. AI automation goes beyond rule-based systems to handle complex, variable tasks that previously required human judgment.
Technologies and strategies for analyzing business data to support better decision-making. AI-enhanced BI tools can automatically identify patterns, predict trends, and generate actionable insights.
A software application that simulates human conversation through text or voice. Modern AI chatbots use natural language processing to understand context and provide relevant responses.
The amount of text an AI model can process at once, measured in tokens. Larger context windows allow the AI to consider more information when generating responses, enabling more coherent long-form content.
A specialized AI agent built on platforms like Genspark, tailored to specific business needs with custom personas, workflows, and integrations. Unlike generic AI tools, Custom Super Agents are designed for particular use cases and industries.
A subset of machine learning using neural networks with multiple layers to learn complex patterns. Deep learning powers most modern AI applications, including image recognition, language understanding, and autonomous systems.
The integration of digital technology into all areas of business, fundamentally changing how organizations operate and deliver value. AI transformation is a key component of modern digital transformation initiatives.
Numerical representations of text, images, or other data that capture semantic meaning. Embeddings enable AI to understand relationships between concepts and find similar content, powering search, recommendations, and RAG systems.
The process of further training a pre-trained AI model on specific data to improve its performance for particular tasks. Fine-tuning allows businesses to customize general-purpose AI for their unique needs.
A large AI model trained on broad data that can be adapted for many downstream tasks. Examples include GPT-4, Claude, and Gemini. These models serve as the base for building specialized AI applications.
AI systems that can create new content—text, images, code, audio, or video—based on patterns learned from training data. Generative AI is revolutionizing content creation, design, and software development.
An AI platform that enables the creation of Custom Super Agents—specialized AI assistants with defined personas, capabilities, and workflows. Genspark allows businesses to build production-ready AI agents without extensive coding.
A family of large language models developed by OpenAI. GPT models use transformer architecture and are trained on vast amounts of text to generate human-like responses. GPT-4 is currently among the most capable LLMs available.
When an AI model generates information that sounds plausible but is factually incorrect or made up. Reducing hallucinations is a key challenge in deploying AI for business applications where accuracy is critical.
An AI system design that incorporates human oversight and decision-making at key points. HITL ensures quality control and allows humans to handle edge cases that AI might mishandle.
Connecting AI systems with existing business tools and workflows. Successful AI implementation requires seamless integration with CRMs, ERPs, communication platforms, and other enterprise software.
The process of using a trained AI model to make predictions or generate outputs on new data. Inference is what happens when you interact with an AI—the model applies its learned patterns to your input.
A structured collection of information that an AI system can access to provide accurate, relevant responses. Custom Super Agents often use knowledge bases containing company-specific documents, FAQs, and procedures.
AI models trained on massive text datasets to understand and generate human language. LLMs like GPT-4, Claude, and Gemini power most modern AI assistants and can perform tasks from writing to coding to analysis.
Development platforms that allow users to build applications with minimal or no programming. Many AI tools, including Genspark, offer low-code interfaces for creating AI agents and automations.
A branch of AI where systems learn from data to improve their performance without being explicitly programmed. ML enables AI to recognize patterns, make predictions, and adapt to new situations.
An AI architecture where multiple specialized agents work together, each handling different aspects of a task. Multi-agent systems can tackle complex workflows by coordinating specialized capabilities.
AI technology that enables machines to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation services, and sentiment analysis.
A computing system inspired by the human brain, consisting of interconnected nodes (neurons) that process information. Neural networks are the foundation of modern AI and deep learning systems.
The coordination of multiple AI components, agents, or services to accomplish complex tasks. Orchestration ensures different parts of an AI system work together seamlessly.
The input text or instruction given to an AI model to generate a response. Well-crafted prompts are essential for getting accurate, useful outputs from AI systems.
The practice of designing and optimizing prompts to achieve desired AI outputs. Prompt engineering is a critical skill for building effective AI agents and getting consistent, high-quality results.
The defined character, voice, and behavioral guidelines for an AI agent. A well-designed persona ensures consistent, on-brand interactions and appropriate responses for the intended use case.
A technique that combines AI language models with external knowledge retrieval. RAG allows AI to access and cite specific documents, reducing hallucinations and enabling accurate responses based on your company's data.
A measure of the profitability of an investment. In AI projects, ROI considers time savings, error reduction, scalability, and revenue impact against implementation and operational costs.
An advanced AI agent capable of complex, multi-step tasks with specialized knowledge and defined workflows. Super Agents go beyond simple Q&A to handle sophisticated business processes, integrations, and decision-making. Platforms like Genspark enable businesses to create Custom Super Agents tailored to their specific needs.
AI technique that identifies and extracts emotional tone from text—determining whether content is positive, negative, or neutral. Used for customer feedback analysis, social media monitoring, and market research.
The initial instructions that define an AI agent's behavior, capabilities, and constraints. System prompts establish the agent's persona, knowledge boundaries, and response guidelines before user interaction begins.
The basic unit of text that AI models process—roughly equivalent to 3/4 of a word in English. AI pricing and context limits are often measured in tokens. A 1,000-token response is approximately 750 words.
A neural network architecture that revolutionized AI by enabling models to process sequences of data in parallel and understand context across long texts. Transformers power most modern LLMs including GPT and Claude.
The dataset used to teach an AI model patterns and behaviors. The quality, diversity, and size of training data significantly impact model performance and capabilities.
A specialized database that stores and retrieves data based on similarity rather than exact matches. Vector databases power RAG systems by finding relevant documents based on meaning, not just keywords.
Using AI and software to automate business processes that involve multiple steps, decisions, and systems. AI-powered workflow automation can handle complex, variable tasks that traditional automation cannot.
An AI's ability to perform tasks it wasn't explicitly trained for. Modern LLMs excel at zero-shot learning, handling new types of requests by applying patterns from their general training.
Understanding AI terminology is the first step. Let's discuss how to apply it to transform your business.