Golden abstract architectural details of a modern building facade in Jönköping, Sweden.

Glossary

Speak AI fluently

Plain-language definitions of the terms that matter, written to be used, not to show off. Each entry includes how we think about it in real client work.

Book a consultation Talk to us

Photo: Efrem Efre / Pexels

Models & training

Large Language Model (LLM)

An AI model trained on vast text that predicts language to answer questions, write, summarize, and reason.

Fine-Tuning

Continuing a model's training on your own data so it performs better on your specific tasks or style.

Transformer Architecture

The neural network design behind modern AI that uses attention to weigh how parts of an input relate to each other.

Foundation Model

A large model trained on broad data that can be adapted to many different tasks.

Reinforcement Learning (RLHF)

A training method where models learn from feedback signals, including human preferences, to behave more helpfully and safely.

Training vs Inference

Training is teaching a model from data; inference is using the finished model to make predictions.

Model Distillation

A technique that trains a smaller, faster model to mimic a larger one, cutting cost while keeping much of the quality.

LoRA / QLoRA

Efficient fine-tuning methods that adapt a large model by training small add-on weights instead of the whole thing.

Federated Learning

Training a shared model across many devices or sites without the raw data ever leaving them.

Synthetic Data

Artificially generated data that mimics real data, used to train or test AI when real data is scarce or sensitive.

Data Labeling

Tagging raw data with the correct answers so a model can learn from it or be measured against it.

Annotation

Adding descriptive markup to data, such as boxes on images or tags on text, to make it usable for AI.

Transfer Learning

Reusing a model trained on one task as the starting point for a related task, instead of training from scratch.

Techniques

Retrieval-Augmented Generation (RAG)

A pattern that fetches relevant documents from your data and feeds them to a model so answers are grounded and citable.

Prompt Engineering

The practice of crafting instructions, examples, and context that reliably steer a model toward the output you want.

Tool Use (Function Calling)

Letting an AI model call external functions, APIs, or systems to take real actions and fetch live data.

Chain of Thought

Prompting a model to reason step by step before answering, which improves accuracy on hard problems.

Semantic Search

Search that matches on meaning rather than exact keywords, so it finds relevant results even when wording differs.

Temperature

A setting that controls how random or predictable a language model's output is, from focused to creative.

Active Learning

Letting the model pick the most useful examples for humans to label, so you train more with less labeling.

Few-Shot Learning

Teaching a model a new task with just a handful of examples shown in the prompt, no retraining needed.

Zero-Shot Learning

Getting a model to do a task with no examples at all, working purely from an instruction.

Core concepts

AI Agent

An AI system that plans multiple steps and uses tools to complete tasks, not just answer a single question.

Agentic AI

AI that pursues goals autonomously by planning, using tools, and adapting across multiple steps with minimal hand-holding.

Embeddings

Numeric representations of text or images that place similar meanings close together, enabling search and retrieval.

Hallucination

When an AI model states something false or made-up while sounding completely confident.

AI Alignment

Making sure an AI system actually pursues the goals and values its operators intend.

Multi-Modal AI

AI that can understand and combine more than one type of input, like text, images, and audio.

Knowledge Graph

A structured map of entities and the relationships between them that machines can reason over.

AI Operating System

A unified layer that connects models, data, tools, and workflows so AI runs reliably across an entire organization.

Natural Language Processing (NLP)

The field of AI focused on getting computers to understand, interpret, and generate human language.

Computer Vision

The field of AI that lets computers interpret images and video, identifying objects, text, and patterns in them.

Token

The basic chunk of text a language model reads and writes, often a word, part of a word, or punctuation mark.

Context Window

The maximum amount of text, measured in tokens, a model can consider at once when generating a response.

AI Readiness

How prepared your data, people, and processes are to adopt AI successfully.

Chatbot vs AI Agent

A chatbot talks; an AI agent plans and takes multi-step action with tools.

Operations & MLOps

MLOps

The engineering practice of deploying, monitoring, and maintaining AI systems so they stay reliable in production.

Vector Database

A database built to store embeddings and find the most semantically similar items fast, powering search and RAG.

Inference

The act of running a trained model to produce an output, such as generating an answer from a prompt.

Quantization

Shrinking a model by storing its numbers at lower precision, making it faster and cheaper to run with minimal quality loss.

Data Drift

When live data gradually diverges from what a model was trained on, quietly eroding its accuracy.

Model Monitoring

Continuously tracking a deployed model's quality, behavior, and cost so problems are caught before users feel them.

AI-as-a-Service (AIaaS)

Renting AI capabilities through cloud APIs instead of building and hosting the models yourself.

Edge AI

Running AI directly on local devices instead of sending data to the cloud for processing.

Benchmark

A standardized test or dataset used to measure and compare how well AI models perform on a task.

Evaluation

The process of measuring whether an AI system produces correct, safe, and useful outputs for its intended task.

Latency

The time it takes an AI system to return a response after receiving a request, usually measured in milliseconds or seconds.

Throughput

The volume of requests or tokens an AI system can process in a given period, such as requests or tokens per second.

Cost per Token

The price charged for each unit of text an AI model reads or generates, the core pricing unit for most language models.

Governance & compliance

AI Governance

The policies, roles, and controls that ensure AI is used safely, lawfully, and accountably across an organization.

AI Red Teaming

Deliberately attacking your own AI system to find failures before real users or attackers do.

Guardrails

Rules and checks that constrain what an AI system is allowed to say or do.

AI Safety

The practice of building AI systems that behave reliably and avoid harm, both today and as they grow more capable.

Responsible AI

A set of practices for building AI that is fair, transparent, accountable, and respectful of privacy and people.

Explainable AI (XAI)

Techniques that make an AI system's outputs understandable, so people can see why it decided what it did.

Bias in AI

Systematic unfairness in AI outputs, usually inherited from skewed training data or flawed design choices.

SOC 2 for AI

Applying the SOC 2 trust criteria for security, availability, and confidentiality to AI systems and their data.

HIPAA for AI

Meeting US health-privacy rules when AI systems handle protected health information.

EU AI Act

The European Union's law that regulates AI by risk level, from banned uses to strict rules for high-risk systems.

NIST AI RMF

A voluntary US framework for identifying and managing AI risks across the system lifecycle.

ISO 42001

The first international standard for managing AI responsibly, defining an auditable AI management system.

PCI-DSS for AI

Applying payment card security rules to AI systems that touch cardholder data.

PIPEDA for AI

Meeting Canada's federal privacy law when AI systems handle personal information.

AI Policy

Your organization's written rules for how AI may and may not be used.

Shadow AI

Unsanctioned AI tools employees use without IT or security oversight.

Let's build the intelligence that moves your business.

Tell us where you're headed. We'll show you what's possible, and exactly how we'd get there together.

Book a discovery call Talk to us

AI