Plain English Guide to AI Terminology

Text graphic with "Complex AI Terms Explained." Colorful hexagons list terms like "Generative AI," "RAG," "Machine Learning." Dark background.

We talk a lot about AI, automation, and chatbots, but the jargon can feel like a foreign language. We have found that organisations are more likely to embrace AI if they can understand the concepts, even at a high level. You don't need to know how everything works under the hood to be able to take advantage of the amazing opportunities that AI offers. With that in mind, we created our ReganByte Plain English Guide to AI to help with that journey.

Below is a simple table of common AI terms, explained in plain English with relatable examples that should help demystify the world of AI whether you are part of a business, a healthcare organisation, or a charity.

Plain English Guide to AI Table

Term	Simple Explanation	Example
Artificial Intelligence (AI)	Technology that allows machines to mimic human thinking and decision-making.	A chatbot answering customer queries without human help.
Machine Learning (ML)	A system that improves by learning from past data and interactions.	A chatbot getting better at understanding Irish place names.
Natural Language Processing (NLP)	Helps chatbots understand and respond in everyday language.	You type "What's the weather like?" and get a clear answer.
Intent Recognition	The chatbot's ability to figure out what you're asking.	Typing "Book a meeting" triggers the chatbot to start scheduling.
Entities	The key details in a question or command.	In "Schedule a call with John tomorrow," John and tomorrow are entities.
Generative AI	AI that creates text, images, or other content in response to prompts.	A chatbot that generates full responses or drafts emails for users.
Dialogue Management	Controls how the chatbot keeps the conversation flowing logically.	The chatbot follows up with, "Would you like to add a location?" after booking an appointment.
Training Data	Real conversations and examples that teach the chatbot how to respond.	Providing sample questions from real customer emails to improve chatbot responses.
Large Language Models (LLMs)	Massive AI models trained on huge amounts of text, used to generate human-like replies.	ChatGPT or Google Gemini responding with detailed answers.
Hallucinations	When an AI chatbot gives an incorrect or made-up answer.	A chatbot confidently explaining a fact that isn't true.
Retrieval-Augmented Generation (RAG)	A system that combines Generative AI with up-to-date knowledge from reliable databases. This combats hallucinations.	Dwayne using the information in the pre-defined knowledge base for its answers.
Multimodal AI	AI that can handle text, images, and audio all together.	Uploading an image and asking a chatbot to describe it.
Reasoning Models	Advanced AI that can solve more complex problems by reasoning, not just predicting text.	An AI chatbot solving a multi-step medical query.
Chatbot Frameworks	Toolkits that developers use to build chatbots.	Using Microsoft's Azure Bot Framework to create a chatbot for customer support.
Integration	Connecting chatbots to websites, CRMs, or other systems.	A chatbot that can pull data from your calendar to suggest meeting times.
User Experience (UX)	How pleasant and helpful the chatbot feels to use.	Simple, clear responses that help users quickly find answers.
Personalisation	Tailoring chatbot responses based on user data or past interactions.	A bot that remembers your name and preferences after your first visit.
Voice Recognition	Technology that allows chatbots to understand spoken commands.	Saying "Find my nearest pharmacy" into your phone and the chatbot responds.
Sentiment Analysis	The chatbot's ability to detect whether you're happy, frustrated, or concerned.	Responding more gently if the user seems upset, or triggering a hand-off to a human agent if a customer is angry.
Autonomous AI Agents	AI systems that can perform tasks on their own without constant instruction.	An AI agent that books and confirms a meeting automatically, and does research for you before the call.
Ethical AI	Ensuring fairness, privacy, and transparency in how AI systems are built and used.	Chatbots that are reviewed for bias and clearly explain when they are providing advice.
Prompt Engineering	The practice of crafting clear and specific instructions or prompts for AI.	Changing "Tell me about AI" to "Explain how RAG works in AI chatbots with an example."
Confidence Score	A numerical measure showing how certain the AI is in its response.	A chatbot may return an answer with 92% confidence, flagging when it's less sure.
Fallback Response	The default message when a chatbot doesn't understand the input.	"Sorry, I didn't catch that. Could you rephrase your question?"
Context Awareness	The chatbot's ability to remember what has been said earlier in the conversation.	If you ask "What's the weather?" and then follow with "What about tomorrow?" and the chatbot remembers that you are asking about weather.
Multilingual AI	AI systems that can understand and respond in multiple languages.	A chatbot that answers questions in both English and Irish, depending on the user.
Transfer Learning	Reusing an AI model trained on one problem for a new but related problem.	A chatbot trained on customer support data also being used for HR queries.
Intent Mapping	The process of linking user input to specific chatbot actions or responses.	"Book appointment" maps to calendar booking functionality.
Bot Persona	The human-like personality or tone crafted for a chatbot to match the brand.	Dwayne uses an approachable, empathetic tone suited for healthcare queries.
API Integration	Connecting the chatbot to external apps or services using APIs.	A chatbot that checks real-time appointment availability via API.

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Why Plain English Matters

When people understand the technology, they use it better. At ReganByte, we help organisations design chatbot solutions that feel human, clear, and approachable.

Dwayne, our AI chatbot for the vasculitis community, uses RAG technology to pull from up-to-date, expert-reviewed medical content, not guesswork. This builds trust and reliability.