AI & Automation Glossary

The simulation of human intelligence in machines that are programmed to think and learn like humans. AI systems can perform tasks such as visual perception, speech recognition, decision-making, and language translation.

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. ML algorithms build mathematical models based on training data to make predictions or decisions.

A subset of machine learning that uses neural networks with multiple layers (hence 'deep') to learn and make intelligent decisions. It's particularly effective for image recognition, natural language processing, and complex pattern recognition.

A branch of AI that helps computers understand, interpret, and manipulate human language. NLP enables applications like chatbots, language translation, sentiment analysis, and text summarization.

A type of AI model trained on vast amounts of text data to understand and generate human-like text. Examples include GPT, Claude, and other advanced language models used in chatbots and content generation.

An autonomous software program that can perceive its environment, make decisions, and take actions to achieve specific goals. AI agents can work independently or collaborate with humans to automate complex tasks.

The practice of designing and refining input prompts to get the best results from AI models. Effective prompt engineering involves crafting clear, specific instructions that guide AI to produce desired outputs.

The process of training a pre-trained AI model on a specific dataset to adapt it for particular tasks or domains. Fine-tuning improves model performance for specialized use cases.

A field of AI that enables machines to interpret and understand visual information from the world. Applications include image recognition, object detection, facial recognition, and medical image analysis.

AI systems that can create new content, including text, images, audio, video, and code. These models generate original outputs based on patterns learned from training data.

An AI technique that combines information retrieval with text generation. RAG systems retrieve relevant information from a knowledge base and use it to augment the context for a language model, resulting in more accurate and up-to-date responses. This is particularly useful for reducing hallucinations and providing domain-specific answers.

A specialized database designed to store and query high-dimensional vectors (embeddings). Vector databases enable efficient similarity searches, making them essential for RAG systems, recommendation engines, and semantic search applications.

Numerical representations of text, images, or other data that capture semantic meaning. Embeddings convert data into vectors in a high-dimensional space where similar items are positioned close together. They're fundamental to modern AI applications like semantic search and RAG.

The basic unit of text that AI language models process. Tokens can be words, parts of words, or even characters, depending on the tokenization method. Understanding tokens is important for managing API costs and model limitations, as most AI services charge per token.

When an AI model generates information that is incorrect, nonsensical, or not grounded in its training data. Hallucinations are a common challenge with LLMs and can be reduced through techniques like RAG, fine-tuning, and better prompt engineering.

A deep learning architecture that has revolutionized NLP. Transformers use attention mechanisms to process sequences of data, allowing models to understand context and relationships in text. GPT, BERT, and most modern LLMs are based on transformer architecture.

A machine learning approach where a model learns to perform a task after seeing only a few examples. In prompt engineering, few-shot learning involves providing examples in the prompt to guide the model's behavior without retraining.

A technique where a model trained on one task is adapted for a different but related task. This approach leverages pre-trained models (like GPT) and fine-tunes them for specific applications, saving time and computational resources.

A prompting technique that encourages AI models to show their reasoning process step-by-step before providing a final answer. CoT prompting improves accuracy on complex reasoning tasks by breaking problems into smaller, manageable steps.

The ability of AI models to call external functions or APIs to perform actions beyond text generation. This enables AI agents to interact with databases, APIs, and other systems, making them more capable and useful for automation tasks.

AI systems that can process and understand multiple types of input simultaneously, such as text, images, audio, and video. Multimodal models can generate outputs that combine different media types, enabling more sophisticated applications.

AI systems that can autonomously plan, execute, and adapt to achieve goals. Unlike simple chatbots, agentic AI can break down complex tasks, use tools, make decisions, and learn from outcomes, making them suitable for complex automation workflows.

The process of connecting AI-generated content to verifiable sources or facts. Grounding helps reduce hallucinations by ensuring AI responses are based on real data, often achieved through RAG or citation mechanisms.

A parameter that controls the randomness of AI model outputs. Lower temperature values (0-0.3) produce more focused, deterministic responses, while higher values (0.7-1.0) create more creative and varied outputs. Adjusting temperature is crucial for balancing creativity and accuracy.

The maximum amount of text (measured in tokens) that an AI model can process in a single interaction. Larger context windows allow models to consider more information but may increase costs and processing time.

The use of technology to automate repetitive, rule-based business processes. Workflow automation reduces manual effort, minimizes errors, and increases efficiency by executing tasks automatically.

Technology that uses software robots or 'bots' to automate repetitive, rule-based tasks typically performed by humans. RPA can interact with applications, process data, and trigger responses just like a human would.

The connection between different software applications using Application Programming Interfaces (APIs). API integrations allow systems to communicate and share data automatically, enabling seamless automation across platforms.

A way for an application to provide real-time information to other applications. When a specific event occurs, the webhook sends an HTTP request to a specified URL, triggering automated actions.

A popular automation platform that connects different apps and services through 'Zaps' (automated workflows). It enables non-technical users to create automations between thousands of applications.

An open-source workflow automation tool that allows users to connect different services and automate tasks. It provides a visual interface for building complex automation workflows.

A technique that uses event logs to discover, monitor, and improve real processes by extracting knowledge from event logs readily available in today's information systems.

The use of technology to execute recurring tasks or processes in a business where manual effort can be replaced. BPA focuses on automating complex, multi-step business processes.

Development platforms that allow users to create applications and automations with minimal or no traditional programming. These tools use visual interfaces and drag-and-drop functionality.

A data integration process that extracts data from various sources, transforms it to fit operational needs, and loads it into a target database or data warehouse. Often automated to run on schedules.

The delivery of computing services—including servers, storage, databases, networking, software, and analytics—over the internet ('the cloud'). This allows businesses to access resources on-demand without managing physical infrastructure.

A cloud-based software delivery model where applications are hosted by a vendor and made available to customers over the internet. Users typically pay a subscription fee rather than purchasing software licenses.

A set of protocols and tools for building software applications. APIs define how different software components should interact, allowing applications to communicate and share data with each other.

A type of API that follows REST (Representational State Transfer) architectural principles. REST APIs use standard HTTP methods (GET, POST, PUT, DELETE) and are widely used for web services.

A lightweight data-interchange format that's easy for humans to read and write, and easy for machines to parse and generate. JSON is commonly used for transmitting data in web applications and APIs.

An organized collection of structured information or data, typically stored electronically in a computer system. Databases are managed by database management systems (DBMS) and are essential for storing and retrieving business data.

Technology for managing all your company's relationships and interactions with customers and potential customers. CRM systems help businesses stay connected to customers, streamline processes, and improve profitability.

A type of software that organizations use to manage day-to-day business activities such as accounting, procurement, project management, risk management, and supply chain operations.

A series of data processing steps where data is ingested from various sources, transformed, and then loaded into a destination system. Data pipelines automate the flow of data between systems.

An architectural approach where applications are built as a collection of small, independent services that communicate over well-defined APIs. Each service focuses on a specific business function.

A method of packaging software so it can run consistently across different computing environments. Containers include the application code, runtime, system tools, and libraries needed to run the application.

A set of practices that combines software development (Dev) and IT operations (Ops). DevOps aims to shorten the development lifecycle and provide continuous delivery with high software quality.

A performance measure used to evaluate the efficiency of an investment. ROI is calculated by dividing the net profit from an investment by the cost of the investment, expressed as a percentage.

The integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It's also a cultural change that requires organizations to continually challenge the status quo.

The discipline of adjusting a process to optimize some specified set of parameters without violating constraints. The goal is to make processes more efficient, reduce costs, and improve quality.

A measurable value that demonstrates how effectively a company is achieving key business objectives. KPIs are used to evaluate success at reaching targets and help organizations make data-driven decisions.

The capability of a system, network, or process to handle a growing amount of work, or its potential to be enlarged to accommodate that growth. In business, scalability refers to the ability to grow without being hampered by existing structures.

A part-time or contract Chief Technology Officer who provides strategic technology leadership to companies without the cost of a full-time executive. Fractional CTOs offer expertise on-demand for specific projects or ongoing guidance.

A product with just enough features to satisfy early customers and provide feedback for future product development. MVPs help validate ideas quickly and cost-effectively before full-scale development.

An iterative approach to project management and software development that helps teams deliver value to customers faster. Agile teams work in short sprints, adapt to changes, and focus on continuous improvement.