For every business professional, team lead, and decision-maker who's sat through an AI conversation and quietly wondered if everyone else actually understood what was being said.

A client walked into a presentation last year with one clear ask… 

Stop making users navigate. Just give them the answer. 

Not a menu. Not a search bar. Not three clicks to a dashboard they half-understand. The answer — instantly, in context, driven by what the user actually meant to ask.

It was a sharp brief. The right brief, honestly. All intent-driven!

Then the room started talking about how to build it. RAG. Fine-tuning. Orchestration. Workflows. Terms flying around like everyone already knew what they meant and had agreed on definitions before they walked in.

The outcome got muddier the more technical the conversation got. Not because the technology was wrong — but because half the room was nodding through vocabulary they hadn't fully owned yet.

That moment is why this post exists.

Here's the thing mostly don’t say out loud: these aren't developer terms anymore. 

They show up in client presentations, team meetings, leadership reviews, and project kickoffs. Every single week, in every AI conversation happening inside enterprise right now.

The question isn't whether you'll hear them. It's whether you'll know what to do when you do.

1. System Prompting

The briefing your AI reads before you say a word

In plain terms: Like the internal brief a consultant gets before walking into your boardroom — shaping tone, setting limits, deciding what they'll say and what they won't. You never see it. But it's already happened.

Most people think AI starts listening when they type. It doesn't.

There's a layer of instructions loaded before you open the chat — defining what the AI will help with, how it will respond, and what it will quietly avoid. It's running in the background of every tool your team uses today.

You didn't write it. You're usually not shown it. But it's shaping every single answer.

Who wrote the system prompt running inside your enterprise AI right now? Because someone did — and it might not have been your team.

A poorly written system prompt is why your AI sounds robotic to customers. A well-crafted one is why a competitor's tool feels like it actually understands their business.

The output you see is only half the story. The instruction you don't see is the other half.

Where you'll hear it: "We've customised the system prompt for your industry." When someone says this and moves on fast — slow them down. Ask what's in it.

💡 Quick note on "Instructions": Depending on the tool your team uses, you'll hear this called "Custom Instructions" instead of a system prompt. Same concept, different label. ChatGPT calls it Custom Instructions. Claude calls it a system prompt. If someone says "we've set up instructions for the AI" — they're talking about the same layer.

2. Skills

Teaching your AI a specific job — once

In plain terms: Like onboarding a new team member with a specialisation. You don't re-explain it every meeting. They know.

In Claude and ChatGPT, Skills are pre-defined capabilities you build or enable — things the AI can reliably do on demand. Summarise in your brand voice. Always respond in a specific format. Handle a request type the same way, every time.

How many times has your team re-explained the same context to your AI tool this week? If the answer is more than once — that's a skill nobody built yet.

Where you'll hear it: "We've set up skills for our most common use cases." In Claude, this lives inside Projects. In ChatGPT, it's part of GPT customisation. Different names — same idea.

📎 How it all connects to put it simply — and where Artifacts fit in:

1. The system prompt sets the rules for the whole house

2. Skills furnish individual rooms for specific jobs

3. Artifacts are what gets built and refined inside those rooms over time

In Claude, an Artifact is a living output — a document, a plan, a tracker, a brief — that gets created and updated within the conversation as context grows. It carries forward, accumulates decisions, and evolves as the work does.

It's not an answer. It's institutional memory the AI helps you build.

In ChatGPT this is called Canvas. In Claude, it's always seen at the top of chat window. Different names — same shift. The AI isn't just responding anymore. It's building something with you.

How much context does your team re-explain every time a new session starts? 

Every fresh conversation is a blank page — and the AI has no memory of the decisions you made last Tuesday.

3. RAG — Retrieval-Augmented Generation

The difference between AI that guesses and AI that looks it up

In plain terms: It’s like giving your AI a library card to your actual company documents — instead of letting it rely on what it memorised two years ago.

AI models are trained on data up to a certain point and then frozen. Ask about your latest policy update, your current pricing, your internal process from last quarter — and the model either admits it doesn't know, or worse, makes something up that sounds completely reasonable.

That second option is the dangerous one.

RAG connects the AI to your real documents before it answers. Your SharePoint. Your CRM. Your knowledge base. Instead of guessing, it retrieves — then responds.

When your AI gave you that confident, well-worded answer last week — was it pulling from your actual data, or making an educated guess from 18 months ago? Most people can't tell. That's the problem.

The difference between RAG and no RAG isn't subtle. It's the difference between an AI that knows your business — and one that performs like it does, with vague answers.

Where you'll hear it: "Our AI is RAG-powered." "We index your knowledge base in real time." This is the term that separates tools that actually know your business from ones that fake it convincingly.

4. Fine-Tuning

Teaching an AI your language — permanently

In plain terms: Like the difference between handing someone a handbook on day one versus training them for six months until they sound like they've worked there for years.

RAG gives AI access to your documents. Fine-tuning goes deeper — it rewires how the model thinks and responds, based on real examples from your world. Your customer conversations. Your support tickets. Your sales scripts. The model doesn't just retrieve your language. It absorbs it.

The results can be significant. A model fine-tuned on three years of customer service data will handle edge cases a general model won't see coming.

Fine-tuning is the most powerful option in the toolkit. It's also the most expensive one to undo. The teams that use it well start with the smallest possible intervention and work up. The teams that struggle started here when they should have started at system prompting.

Where you'll hear it: "Our model is fine-tuned on your vertical." Before you nod — ask: fine-tuned on what data, from when, and retrained how often?

5. MCP — Model Context Protocol

The plug that connects your AI to everything

In plain terms: Like a USB pen drive for your AI — one standard connection that lets it plug into Salesforce, your ERP, your CRM, your document system. No custom cable needed for every device.

Every enterprise runs on a stack of tools. And every time you want AI to connect to one of them, someone has to build a bridge. A custom integration. A dedicated API. Your IT team quotes six months. The project goes into the backlog.

MCP changes that. It's a standardised protocol — a shared language — that lets AI models plug into external tools without rebuilding that bridge each time. One connection format. Works across systems. And best of all - it doesn’t take much time!

Think about the shift from proprietary chargers to USB-C. Every device, one port. MCP is that moment for enterprise AI — and it's happening right now.

Where you'll hear it: "We're MCP-compatible." "The model connects natively via MCP." This is the term that will define enterprise AI conversations through 2026 and onwards.

6. Orchestration

The conductor deciding which AI does what — and when

In plain terms: Like a traffic control tower at a busy airport — it doesn't fly any of the planes, but nothing takes off, lands, or changes course without it knowing. Remove it, and things collide in ways nobody sees coming.

Most people picture enterprise AI as one model doing everything. In reality, complex AI systems are more like a relay race.

One model figures out what you're asking. Another goes and finds the right information. A third shapes it into a response. A fourth checks it before it reaches you. Each runner does one leg well. Orchestration is what makes sure the baton gets passed cleanly — every single time.

When it works, you don't notice it. The response arrives and feels seamless. When it breaks, it breaks quietly — and the output still looks fine.

Where you'll hear it: "Our platform orchestrates multiple models." "We use an orchestration layer for complex queries." Ask who controls the orchestration logic — and whether you can actually see it.

7. Workflows

The journey map your AI actually walks through

In plain terms: Remember the last time your team mapped out a user journey — every step a person takes, every decision point, every fork in the road depending on what they choose? An AI workflow is exactly that. 

Except instead of a person walking through it, the AI does. Step by step. Decision by decision.

If you've ever been involved in experience mapping or service blueprinting, you already understand the bones of this.

You define the path: a customer message comes in → the AI reads it → decides what kind of request it is → retrieves the right information → drafts a response → checks it against your guidelines → sends it. Every box on that map is a step. Every diamond is a decision point. The AI follows it the same way, every single run.

What makes it powerful is consistency. What makes it risky is the same thing…

Here’s how :

Journey maps get outdated. A path you designed for last year's product, last year's policy, last year's customer — may not reflect how things work today. AI workflows have the same problem. They keep running the old map long after the territory has changed. And unlike a document sitting in a shared drive, nobody notices until something goes wrong.

The question to ask your team isn't just "what does the workflow do?" It's "who owns updating it when the journey changes?"

If you hear this phrase: “We’ve automated that end-to-end with no human in the loop.”  Hold on a minute. 

A workflow without a human checkpoint is like a journey map with no room for detours – and real journeys always have them.

Why all eight travel together.

Here’s what nobody prepares you for.

These terms don’t appear one at a time.

A colleague or vendor will describe a system that uses RAG to pull your documents, runs on a fine-tuned model, connects to your existing tools, manages complex tasks through automated workflows, and is governed by a system prompt you don’t have visibility into.

That’s a real sentence. You’ll hear it soon.

When you hear it, you’ll either nod or ask the one question that matters:

“Which of these is actually doing the work, and how do we know when it gets it wrong?”

The teams winning with AI aren’t the ones with the biggest budgets or the most technical people. 

They’re the ones where at least one person understood enough to ask the right question before the contract was signed, before the workflow went live, and before the system prompt nobody reviewed started shaping every customer interaction.

It doesn’t have to be the most technical person in the room. It just has to be someone.

The best AI decisions in the next two years won’t be made by the most technical person in the room. They’ll be made by the most informed one.