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AI Terms Debunked: Your Glossary to Technical Terminology (without the nonsense)
There’s a lot of language floating around the AI space that sounds more complex than it needs to be. Not because the concepts themselves are hard but because the way they’re explained often assumes a technical background or leans heavily on jargon.
This section exists to do the opposite.
You don’t need to memorise terms or “speak AI fluently”. You just need enough understanding to use these tools intelligently, make good business decisions and stay grounded in your role as the human in charge and so that we don't have to argue with our Chatty G's
Think of this as orientation, not instruction.
Agent
An AI agent is an AI system set up to perform a specific role or task repeatedly, often inside a workflow. Think of it less as a chatbot and more as a specialist assistant with a job description. Agents matter because they reduce decision fatigue by narrowing scope and responsibility.
Artificial Intelligence (AI)
At its core, artificial intelligence refers to software designed to perform tasks that typically require human intelligence. That includes understanding language, recognising patterns, analysing information or assisting with decisions. In business terms, AI is best understood as cognitive support. It helps you think, organise, draft, analyse and accelerate but it does not replace judgement, experience or leadership. When AI starts making decisions for you instead of supporting better decisions, it’s being used incorrectly.
Automation vs Augmentation
Automation replaces a step entirely. Augmentation supports a human in completing it better or faster. Many people try to automate too early and lose quality or clarity. In most coaching and service businesses, augmentation comes first. Automation follows once thinking is clean.
Bias
AI can reflect bias because it was trained on human-created data. This is particularly important when generating content related to people, identity or sensitive topics. AI doesn’t have values. You do. Which is why responsibility, discernment and oversight always sit with the human using the tool.
Context Window
This refers to how much information an AI can “hold” in mind at one time during a conversation. Think of it as working memory. If you exceed it, earlier parts of the conversation may be forgotten or deprioritised. In practical terms, this explains why long, messy chats degrade in quality over time and why structured inputs produce better results. Context is finite. Use it intentionally.
Fine-Tuning
Fine-tuning means training an AI further on a specific dataset so it behaves in a very particular way. Most business owners do not need this. It’s often confused with good prompting or system design. In reality, thoughtful prompts and workflows usually achieve the same outcome with far less cost and complexity.
Generative AI
Generative AI is the category of AI that creates new output. This might be written content, images, audio, video, code or ideas. When you use AI to draft an email, outline an offer, generate content ideas or create visuals, you’re using generative AI. What matters here is that this type of AI doesn’t retrieve a single correct answer. It constructs responses based on patterns. That makes it powerful for drafting and ideation, but it also means discernment is still required.
Hallucinations
When AI confidently produces information that isn’t correct, this is often referred to as a hallucination. It’s not a malfunction. It’s the system filling gaps with the most statistically likely answer. AI will always give you an answer, even when it shouldn’t. This is why human oversight and critical thinking are non-negotiable, particularly when accuracy matters.
Human-in-the-Loop
This means a human remains involved in reviewing, approving or refining AI output. This is not a weakness. It’s best practice. Especially where judgement, ethics or nuance matter. AI is strongest when it supports human decision-making, not when it bypasses it.
Large Language Models
Text-based AI tools are powered by what are called large language models. These models are trained on enormous amounts of text so they can recognise patterns in language and predict what words are likely to come next in a sequence. They don’t understand meaning the way humans do. They predict language based on probability. This is why clarity, structure and context in your input dramatically affect the quality of the output you receive.
Latency
Latency refers to response time. Faster is not always better. Sometimes slower responses indicate deeper reasoning or longer context processing. Understanding this helps reduce frustration when tools behave differently across tasks.
Models
A model refers to the specific version of the AI you’re using. Different models are optimised for different outcomes, such as reasoning, creativity, speed, length or safety. This is why the same prompt can produce different results across different tools. From a business perspective, this isn’t about chasing the newest model. It’s about understanding that tools behave differently and choosing the right one for the task at hand.
Multimodal AI
Multimodal AI can work across different types of input and output — text, images, audio and video. For example, uploading screenshots, analysing visuals or generating imagery from text. This matters because it expands AI beyond writing and into design, review and interpretation tasks.
Overfitting (in simple terms)
Overfitting happens when AI becomes too narrowly focused on a pattern and performs poorly outside it. In business terms, this shows up when people try to force AI into rigid scripts that don’t adapt. Flexibility matters.
Prompts
A prompt is simply what you give the AI to work with. Your instruction, your question, your context. Despite how it’s often framed, prompting isn’t about clever wording. It’s about clear delegation. You are assigning a task to a very fast, very literal assistant that has no sense of priority, nuance or consequence unless you provide it. AI doesn’t fix fuzzy thinking. It reflects it. The clearer your thinking, the better your results.
Prompt Engineering
Despite the name, prompt engineering isn’t technical. It’s the skill of thinking clearly, communicating intent, breaking tasks into steps and guiding output toward a specific outcome. This is a thinking skill, not a tech skill. The better your strategic thinking, the more effective your AI use will be.
Retrieval-Augmented Generation (RAG)
This is when AI pulls from a specific knowledge base (documents, notes, databases) instead of relying only on general training. From a business perspective, this is how AI becomes contextually accurate rather than just articulate. It’s especially relevant for internal documentation, policies, curriculum and client support.
System Prompt
A system prompt is background instruction that shapes how an AI behaves before you ever ask it a question. It defines role, tone, boundaries and priorities. In business use, system prompts are how you align AI to your brand voice, values or way of thinking. This is where AI starts feeling supportive rather than generic.
Temperature
Temperature controls how predictable or creative an AI’s responses are. Lower temperature means safer, more consistent output. Higher temperature introduces variation and creativity. For business tasks that require clarity, accuracy or consistency, lower is usually better. For ideation or brainstorming, higher can be useful. This is a dial, not a moral judgement.
Tokens
AI doesn’t measure language in words the way humans do. It processes text in chunks, which can include parts of words, punctuation and spacing. These chunks are called tokens. Longer or messier inputs use more tokens and can reduce performance. Clear, concise instructions generally produce faster and more accurate outputs. You don’t need to obsess over tokens, just understand that clarity improves efficiency.
Training Data
Training data is the information the AI learned from during development. It’s why the AI knows how language works, but also why it can reflect outdated assumptions, gaps or bias. AI doesn’t learn in real time unless it’s specifically designed to. It generates responses based on its training and your input. This is why AI should be treated as a drafting partner, not a source of truth and why verification matters in business use.
Workflows and Automations
The real power of AI in business isn’t in one-off conversations. It’s in systems. When AI is embedded into repeatable processes such as content creation, onboarding, analysis or client support, it becomes leverage rather than noise. AI on its own can be helpful. AI inside clean, intentional structure is where transformation happens.
Your Takeaway
AI doesn’t reward people who know the most terminology. It rewards people who think clearly, set boundaries and stay in the role of decision-maker. You don’t need to become technical to use AI well. You need to understand how human intelligence, business strategy and machine support work together.
That’s the purpose of this glossary.
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AI Terms Debunked: Your Glossary to Technical Terminology (without the nonsense)
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