These are the building blocks of AI — the terms you will encounter every time you use Claude, Gemini, or any other large language model at work. Understanding these concepts will help you use AI tools more effectively and make better decisions about when and how to apply them.
A large language model is an AI system trained on vast amounts of text data to understand and generate human language. Claude, Gemini, and ChatGPT are all large language models. They learn patterns in language and use those patterns to predict the most relevant response to any given input.
A token is the basic unit of text that an AI model processes. A token is roughly equivalent to three or four characters, or about three quarters of a word in English. AI models read and generate text in tokens, not in words or sentences. Your usage limits and costs are calculated in tokens.
The context window is the maximum amount of text an AI model can read and consider at one time — including everything you have written, any documents you have shared, and its own previous responses. Once you exceed the context window, the model begins to lose track of earlier parts of the conversation.
A prompt is the instruction or input you provide to an AI model to tell it what you want it to do. The quality of the prompt directly determines the quality of the output. A vague prompt produces a generic response. A precise prompt with clear context, role, and constraints produces a useful, targeted response.
A foundation model is a large AI model trained on broad data that can be adapted for a wide range of tasks. Claude and Gemini are foundation models. They are built once at enormous scale and then refined for specific applications, rather than being trained from scratch for each individual use case.
A hallucination is when an AI model generates information that is factually incorrect, invented, or unsupported — but presents it with complete confidence. Hallucinations are a known limitation of all current large language models and are most likely to occur when the model is asked about specific facts, dates, statistics, or names it was not trained on.
Temperature is a setting that controls how predictable or creative an AI model's responses are. A low temperature produces more focused, consistent, and deterministic outputs. A high temperature produces more varied, creative, and sometimes unpredictable outputs. Most enterprise AI tools set a default temperature suited to professional use.
A multimodal AI model can process and generate multiple types of content — not just text, but also images, audio, and video. Gemini is a multimodal model that can natively analyse video recordings and audio files. Claude can process text and images but does not natively handle audio or video.
Fine-tuning is the process of taking a pre-trained foundation model and training it further on a specific dataset to improve its performance on a particular task or domain. A fine-tuned model learns the vocabulary, tone, and patterns of a specific industry or company without needing to be built from scratch.
Latency refers to the time it takes for an AI model to generate a response after receiving a prompt. Smaller, faster models like Claude Haiku have lower latency and are better suited for high-volume or time-sensitive tasks. Larger, more capable models like Claude Opus have higher latency but produce more sophisticated outputs.
These terms describe the techniques and concepts that emerge when professionals actively work with AI tools every day. Understanding these will help you craft better prompts, get more consistent outputs, and build more effective AI workflows for your team.
A system prompt is a set of instructions given to an AI model before the conversation begins. It defines the model's role, tone, constraints, and behaviour for the entire session. In Claude Projects, the system prompt acts as a permanent background instruction that shapes every response the model gives to your team.
Grounding refers to connecting an AI model's responses to specific, verifiable source documents rather than relying on its general training data. A grounded response cites real documents and pulls factual data from those sources. This dramatically reduces hallucinations and makes outputs more trustworthy for corporate use.
RAG is a technique that combines a large language model with a search system that retrieves relevant documents from a database before generating a response. The model uses the retrieved documents as context, producing answers that are grounded in specific, up-to-date sources rather than relying solely on its training data.
Few-shot prompting is a technique where you include two or three examples of the desired input-output format directly inside your prompt. The model learns the pattern from your examples and replicates it for the new task. This is one of the most reliable ways to control the format and tone of AI outputs.
Zero-shot prompting means asking an AI model to complete a task without providing any examples — relying entirely on the model's training to understand what is expected. Modern large language models are capable of strong zero-shot performance on many professional tasks when the prompt is clear and specific.
Prompt chaining is a technique where you break a complex task into a sequence of smaller, connected prompts — using the output of one prompt as the input for the next. This approach produces higher quality results for complex workflows because each step can be verified and refined before proceeding.
Instruction following refers to how precisely a model adheres to the specific requirements stated in a prompt. A model with strong instruction following completes exactly what was asked — no more, no less — without adding unrequested content, ignoring constraints, or reinterpreting the task. Claude is widely regarded as having particularly strong instruction following among enterprise models.
Output variance describes the degree to which an AI model produces different responses when given the same prompt multiple times. High variance means the model gives noticeably different answers on each run. Low variance means the outputs are consistent and predictable. For enterprise workflows requiring reliable, repeatable outputs, low variance is preferable.
Iterative refinement is the process of improving an AI output through a series of follow-up prompts rather than trying to get the perfect result in a single interaction. Each follow-up prompt builds on the previous output, correcting errors, adjusting tone, or adding missing elements. This approach consistently produces higher quality results than single-shot prompting.
Prompt engineering is the practice of designing and refining prompts to reliably produce high-quality AI outputs. It involves understanding how models interpret language, what context to provide, how to set constraints, and how to structure requests for maximum clarity. For corporate professionals, prompt engineering is a practical skill that directly improves the quality and consistency of AI outputs.
These terms cover the governance, privacy, and safety dimensions of AI that matter most to IT leaders, compliance officers, and senior management. Understanding these concepts is essential for deploying AI tools responsibly in a corporate environment.
Data residency refers to the physical location where data is stored and processed. Many enterprises and regulated industries — particularly in the UK and EU — require that company data remain within specific geographic boundaries to comply with regulations such as UK GDPR. Enterprise AI providers offer data residency controls that restrict where prompts and outputs are stored.
Constitutional AI is a training methodology developed by Anthropic for Claude. The model is trained to follow a set of principles — a constitution — that guides it toward being helpful, harmless, and honest. Instead of relying solely on human feedback, the model learns to evaluate and refine its own responses against these principles, making it more consistent and predictable in enterprise settings.
An agentic workflow is a process where an AI model takes a sequence of autonomous actions to complete a multi-step task — rather than simply responding to a single prompt. An AI agent can search the web, read files, write code, send emails, or call external systems as part of completing a goal. Agentic workflows represent the frontier of enterprise AI deployment in 2026.
This refers to whether an AI provider uses the content of your conversations to improve or retrain their models. On free and some consumer tiers, providers may use conversation data for training. On enterprise tiers, both Anthropic and Google commit that user data is not used to train their models. This distinction is critical for organisations handling confidential information.
AI governance refers to the policies, controls, and oversight mechanisms an organisation puts in place to ensure that AI tools are used safely, ethically, and in compliance with regulations. This includes defining approved use cases, setting data handling rules, establishing review processes, and assigning accountability for AI outputs.
An audit log is a record of all AI interactions within an organisation — who used the tool, when, and what prompts and outputs were generated. Enterprise AI platforms provide audit logs to support compliance, incident investigation, and governance. Audit logs are a requirement for regulated industries such as financial services, healthcare, and legal.
In the context of enterprise AI deployment, an organisational unit is a group of users whose AI access can be configured independently by an administrator. Google Workspace admins can enable or disable Gemini features for specific organisational units — for example, enabling full Gemini access for the Marketing team while restricting it for Legal until a compliance review is complete.
AI adoption maturity describes how advanced an organisation is in its use of AI tools — from basic individual use through to organisation-wide deployment with governance frameworks, custom models, and agentic workflows. Most corporate teams in 2026 are at an early to intermediate stage, using AI for individual productivity tasks rather than fully integrated enterprise workflows.
Model Context Protocol is an open standard developed by Anthropic that allows AI models to connect securely to external tools and data sources. MCP enables Claude to read from and write to external systems — databases, APIs, file systems, and business applications — without requiring custom integrations for each connection. It is the foundation for enterprise agentic workflows.
AI value realisation refers to the measurable business benefits an organisation derives from deploying AI tools — including time saved, cost reduced, quality improved, and revenue generated. Organisations that invest in structured AI training, governance, and workflow integration consistently realise more value than those that allow unstructured individual adoption without measurement.