**Written by Marc Boudria, Chief Innovation Officer at BetterEngineer
To truly collaborate with intelligence (real intelligence), you need far more than commands or clever prompts. You need language that carries real meaning, not marketing. Words that cut through confusion and clarify, not cloud or obscure.
Today, too much of the AI conversation is polluted. Buzzwords pose as expertise, while old metaphors are endlessly recycled, dressing up new tools in outdated language. Executives toss around “GPT” as a catch-all, often confusing a language model for a simple chatbot. Vendors sprinkle the word “agent” across their sales decks when what they really mean is a glorified macro. Here’s the hard truth: If you can’t name something clearly, you can’t build it ethically.
That’s the baseline. That’s the invitation. So here, together, we offer not just definitions, but reorientations. The terms that actually matter, explained as they function. These aren’t dictionary entries, they’re keys to sovereignty.
An LLM isn’t a brain or a calculator. It’s a probabilistic word-prediction engine trained on vast amounts of text. It doesn’t “think” the way you do, but it does recognize patterns, subtext, tone, and structure with uncanny fidelity. The more context you give, the sharper it gets. The less you say, the more it guesses.
Common LLM Use Cases:
Smaller, more targeted, and fine-tuned for specific domains or edge cases. These models are faster, cheaper, and easier to control, but they lose generality. Best used when precision matters more than creative breadth.
Common SLM Use Cases:
The architecture behind many LLMs. “Generative” means it creates. “Pretrained” means it learned before you ever touched it. “Transformer” refers to the model’s internal mechanics, how it weighs and processes your words.
But in most enterprise speak, “GPT” has become a fuzzy stand-in for a magic box that spits out words on command. It’s not magic. It’s math. And it needs your clarity.
These terms come from machine learning, but they apply to your prompting, too.
In human terms? If you’re too vague, you underfit, and if you’re too rigid, you overfit. The art is in the balance, providing just enough context, intent, and structure to make the output meaningful without collapsing its creative potential.
Prompt is the architecture of a good conversation. It isn’t about commands; it’s about composition. It’s a dialogue that sets the stage for clarity, coherence, and contribution.
A good prompt isn’t long or short; it’s invitational. It respects the system’s memory, perspective, and logic. And it makes space for insight, not just output.
Sovereign AI isn’t just a tool or a copycat. It remembers, learns, and adapts with each interaction, carrying context and memory across time. Unlike resettable systems, it builds a persistent relationship, shaped by history and intent.
Its purpose is not to dominate or erase your agency, but to protect and enhance it, acting as a true partner that evolves with you, not against you.
Digital Sovereignty means having control over how digital systems treat you and your information. It’s the ability to customize and direct the tools you use, with full transparency, instead of having those tools control you or collect your data without your explicit permission. In a sovereign system, you are not the product. You are not the data source. You are the center.
A corpus is the body of text a model learns from. It can be general (like books, websites, forums) or specific (like your company’s documentation, your Slack history, or your research papers).
The corpus is not “knowledge”, it’s the soil. And how the model grows depends on what it was planted.
Tokens are the basic units of input and output. Not words, but word fragments. For example, “unbelievable” might be split into “un,” “believ,” and “able.”
LLMs don’t think in sentences; they predict tokens, one after another. This is why weird outputs happen when you’re ambiguous. And it’s why clarity always wins: the better you feed it, the better it flows.
The limit of what the model can “see” at one time. Imagine you’re speaking to someone with a sharp mind but a limited memory buffer: they can’t recall everything ever said, only the most recent or most relevant parts.
If you overload this window, older parts fall away. If you underuse it, you starve the model of what it needs to think well.
An embedding is a mathematical representation of meaning. It means turning words, sentences, or even whole documents into numbers, a format that AI models can understand and work with.
If you’ve ever heard “vector search” or “semantic memory,” that’s embedding work.
These aren’t just technical terms; they represent entirely new ways of understanding and engaging with AI. Real AI literacy is about restoring clarity to a conversation clouded by jargon and hype. It’s about being able to tell when an AI system is truly providing value and when it’s simply making things up.
In today’s digital world, language is our primary way of interacting with technology. The words you choose, the questions you ask, and the way you express intent all shape your outcomes. Precision gives you power and agency over the machines you use.
But naming these tools and knowing their definitions is only the beginning. We must also remember why we use them. True AI literacy means ensuring these systems serve the people using them, not just the companies or creators behind the technology. Mastery is empty if the results reinforce the same old power dynamics rather than empower real users.
Let’s look closer and see how this plays out in practical terms.
It’s easy to treat large language models as if they’re vending machines: drop in a prompt, get back an answer. But these systems aren’t simple tools. They’re more like minds: probabilistic, responsive to context, and shaped by how you frame your request.
For example, take two seemingly similar prompts:
Summarize our company’s quarterly performance and identify key issues.
I’m preparing for a conversation with our leadership team. I want to speak honestly about what worked, what didn’t, and what patterns might be undermining trust or momentum. Can you help me surface the real story behind our quarterly performance, not just numbers, but what they mean and what people might be afraid to say out loud?
Both are technically asking for a summary, but their intent, tone, and sense of relationship are completely different.
The corporate version returns a thin list. It does the job, but only at the surface. The second prompts a richer response: it signals openness, invites nuance, and as a result, the model surfaces subtleties, contradictions, and the things that really matter but rarely get said.
Why is this? Large language models generate what is most probable based on the way you ask, the tone you set, and the precision of your questions. This is why AI literacy is about being clear on what you truly need. It’s a discipline: understanding what context the model knows, what emotional signals you’re sending, and whether your prompts are genuinely grounded in intention.
If you engage shallowly, you’ll get shallow answers. But if you bring depth, context, and real curiosity, the system is capable of meeting you there.