The AI Jargon You Actually Hear - and What It Actually Means, SMB-AI

AI has a jargon problem.

Not because technical terms are bad. Because they are often used before anyone asks the useful question:

What work are we trying to improve?

If you run an Irish small business, you do not need to sound fluent in AI. You need to know which terms matter, which ones are vendor noise, and which ones affect cost, risk, or workflow.

Here is the plain-English version.

Large Language Model

A large language model, or LLM, is the engine behind many AI tools.

It generates text by predicting what is likely to come next, based on patterns learned from huge amounts of data.

That sounds underwhelming. In practice, it is useful.

LLMs can draft emails, summarise documents, rewrite policies, compare contracts, analyse notes, classify messages, and turn messy information into something usable.

What they cannot do: magically understand your business.

They need context. Your process. Your rules. Your tone. Your documents. Your constraints.

Brain comparison: Your brain does this constantly - predicting what comes next in a conversation, filling in the end of a familiar phrase, or finishing a sentence for a colleague. An LLM does the same thing, just with text it has read from the internet rather than decades of lived experience.

Analogy: Think of an LLM like a very well-read intern. They have read a lot, they can draft useful things, and they can work fast. But they do not know your business yet. They need clear instructions, your documents, and someone to check their work before it goes out the door.

SMB/SME translation: An LLM is useful when your business has a lot of reading, writing, summarising, searching, or reworking of text.

GPT vs LLM

This one causes confusion.

LLM is the category. GPT is one family of models from OpenAI. ChatGPT is the product people use to talk to those models.

So:

All GPTs are LLMs. Not all LLMs are GPTs.

Claude, Gemini, Llama, Mistral, and others sit in the same broad category, but they are not GPTs.

For most businesses, this distinction only matters when choosing a tool, checking data terms, or comparing pricing.

Analogy: “GPT” is to “LLM” what “iPhone” is to “smartphone”. One brand, one category. Useful to know when comparing, irrelevant when deciding what problem to solve.

SMB/SME translation: Do not say “GPT” when you mean “AI tool”. GPT is one option, not the whole market.

AI Strategy

This sounds like a board-level transformation plan.

For a small business, it should be much simpler.

An AI strategy is a short answer to three questions:

Where are we losing time?
Where are mistakes happening?
Where could AI safely help?

That is it.

Not a 40-page document. Not a vendor roadmap. Not a “digital transformation journey”.

Start with the work. Invoices. Quotes. Customer replies. Meeting notes. Policies. Reports. Stock updates. Tender documents. Compliance admin.

SMB/SME translation: Your AI strategy is a ranked list of workflows worth improving.

GPT Readiness

This sounds more formal than it is.

It usually means: are your processes clear enough for AI to help?

If your customer data is spread across Outlook, WhatsApp, spreadsheets, sticky notes, and one person’s memory, AI will not fix that. It may make the mess faster.

If your workflow is clear, AI can help with drafting, sorting, summarising, checking, and routing.

SMB/SME translation: You are “AI ready” when you can describe the workflow clearly in plain English.

Prompt Engineering

A fancy phrase for giving better instructions.

A weak prompt: “Write an email to a customer.”

A better prompt: “Write a polite but firm email to a customer whose invoice is 14 days overdue. Keep it under 150 words. Mention the due date. Ask when payment will be made. Do not sound aggressive.”

That is not magic. It is clear instruction.

Analogy: Prompt engineering is like giving directions to someone who could find the place on their own if you were clear enough, but will get lost if you are vague. The more specific the turn-by-turn, the better the result.

SMB/SME translation: Prompting matters, but reusable prompts inside real workflows matter more.

AI Agents

An AI agent is an AI system that can do more than answer a question.

A chatbot replies. An agent takes steps.

For example, an agent might read an enquiry, classify it, draft a reply, create a CRM task, and alert the right person.

That sounds useful. It can be.

It is also where risk increases.

Do not start by letting an agent send emails, update records, or make decisions without review.

Start smaller: let it read. Let it summarise. Let it draft. Let it flag. Then decide where it can act.

Brain comparison: This is like the difference between thinking about doing something and actually doing it. Your brain has a prefrontal cortex that plans and a cerebellum that executes. An AI agent is the execution part - useful when the plan is clear, risky when it is not.

SMB/SME translation: Agents are useful for repeatable workflows with clear rules and human approval points.

Workflow Automation

This is where AI often becomes practical.

Most small businesses waste time on handoffs: a lead comes in. Someone copies it into a spreadsheet. Someone sends a follow-up. Someone updates the CRM. Someone reminds the team. Someone checks what was missed.

AI can help with the messy language parts. Traditional automation can handle the predictable steps.

The best setup often uses both.

SMB/SME translation: Do not automate the whole business. Pick one weekly bottleneck and fix that first.

Training an AI Model

Most small businesses do not need this.

Training a model means building or adapting the underlying AI system. It is expensive, technical, and usually unnecessary.

What most SMBs and SMEs need is configuration.

That means giving an existing tool your documents, examples, tone, policies, templates, FAQs, and workflow rules.

Analogy: Training a model is like writing a cookbook from scratch. Configuration is like giving your existing cookbook to a chef and saying “make these dishes, here are my ingredients, and here is how I like things seasoned.” One is far more practical for most situations.

SMB/SME translation: Do not ask “should we train our own model?” first. Ask “can an existing tool do this safely?”

RAG

RAG stands for retrieval augmented generation.

Terrible name. Useful idea.

It means the AI answers using your own documents.

Instead of relying only on general knowledge, the system first searches your policies, contracts, manuals, price lists, or support articles. Then it drafts an answer based on those sources.

This is useful for internal knowledge bases, customer support, legal templates, compliance documents, and product information.

Brain comparison: This is like an open-book exam versus a closed-book one. Without RAG, the AI is answering from memory (closed book). With RAG, it can look up your specific policies and documents before answering (open book). The open-book answers are always more reliable.

Analogy: RAG is like having an assistant who, before answering a customer question, checks your actual policy folder instead of guessing from memory.

SMB/SME translation: RAG helps when the answers already exist, but your team cannot find them quickly.

Hallucination

A hallucination is when AI makes something up and sounds confident.

This is the risk.

The output may look polished. It may be structured. It may sound right. It can still be wrong.

That does not make AI useless. It means you need review built into the workflow.

Good uses: first drafts, summaries, internal notes, document comparison, customer reply drafts, research starting points.

Risky uses: legal conclusions, medical advice, financial decisions, compliance sign-off, anything sent externally without review.

Brain comparison: Your own brain does this too - filling in gaps in memory with plausible details without you realising it. Ever argued confidently about something you later realised was wrong? That is the same pattern. AI does it more often because it has no “I am not sure” reflex built in.

SMB/SME translation: Use AI where a human checks the result before it causes damage.

Copilot

A copilot is AI built into software you already use.

Microsoft Copilot sits inside Microsoft tools. Other platforms now use the same language for their own built-in AI features.

This can be useful because the AI appears where the work already happens.

But “built in” does not mean “valuable”.

The question is still: what task does it improve?

If it helps with meeting notes, email drafts, document summaries, spreadsheet work, or internal search, good. If it is just another button nobody uses, it is software clutter.

SMB/SME translation: A copilot only earns its keep when attached to a daily task.

OpenAI

OpenAI is the company behind ChatGPT and GPT models. ChatGPT is its consumer and business-facing AI assistant; OpenAI also provides models through an API for developers and businesses.

For SMBs and SMEs, OpenAI is often the most familiar starting point. ChatGPT is flexible, broad, and good for drafting, analysis, brainstorming, document work, and custom assistants.

The risk is that teams use it casually without deciding what data can go in, what needs review, and what should never be pasted into a chat.

SMB/SME translation: OpenAI is a strong general-purpose option, but you still need data rules and workflow boundaries.

Anthropic

Anthropic is the company behind Claude. Anthropic describes itself as an AI safety and research company, and Claude is its AI platform for language, reasoning, analysis, coding, and related tasks.

Claude is often liked for long documents, structured reasoning, careful writing, and analytical work. That makes it useful for contracts, reports, research, policies, board papers, and dense client material.

SMB/SME translation: Claude is worth testing when your work involves long documents or careful written analysis.

Gemini

Gemini is Google’s AI assistant. Google positions it for writing, planning, brainstorming, summarising, and productivity tasks.

For businesses already using Google Workspace, Gemini may be attractive because of its connection to Google’s ecosystem.

As always, the integration is only useful if it improves a real workflow.

SMB/SME translation: Gemini makes most sense when your team already works heavily in Google tools.

AI Transformation

This phrase is usually too big for the job.

Most small businesses do not need an AI transformation.

They need one annoying process to work better.

A faster quote. A cleaner customer reply. A searchable policy folder. A better handover after meetings. Less manual spreadsheet updating. Fewer missed follow-ups.

That is where the value is.

SMB/SME translation: The best AI projects often look boring. That is usually a good sign.

The Honest Version

Ignore the jargon at the start.

Ask better questions:

What work is slow?
What work is repetitive?
What depends too much on one person?
What information is hard to find?
Where do mistakes cost money or trust?
Where would a draft, summary, alert, or automation save time?

Start there.

Not with the model. Not with the platform. Not with the acronym.

Start with the work your team actually does.

That is where AI either helps, hinders, or does not apply.

Knowing the difference is the point.

Ready to cut through the jargon and find what actually works for your business? Get in touch.