What’s a word you’ve never used aloud, but feel?

Now that we’re inviting more AI agents into our daily lives and workspaces, we need to understand what we’re actually inviting in. And language is a big part of that.

I’ve written this post to help you understand this new area—not just technically, but personally. To make it easier to relate to these tools, and to yourself, through language.

We’re living in a time when language is changing.
With AI tools like large language models, we now speak with systems that learn from our words.
These tools show us something surprising—how much of our thinking begins with language.
And maybe, how many words we haven’t yet used.
Words we’ve felt, but never spoken.
This is a chance to explore those.
To grow our language—and with it, our understanding.

If you’d like to explore, here are a few gentle ways to begin:

• Ask a large language model something personal—like how to describe a feeling you’ve never named. You might be surprised by what it reflects back.
• Try writing a sentence with a word you don’t often use, then ask the model to build on it. Let it stretch your expression.
• Share a story or memory with the model, and ask it to describe the emotion behind it in different ways. Notice how language shifts feeling—and how you connect more deeply with what was already inside you.
• Or just start a quiet dialogue with it. Treat it like a thoughtful assistant, not to impress—but to explore. Language is its native element.

What Makes Large Language Models So Unique?

Large language models like GPT are trained on vast collections of text—books, articles, websites, and more. GPT-4, for example, was trained on hundreds of billions of words, learning not just vocabulary, but the rhythm of human thought.

They don’t think or feel like we do. But they reflect our language back with incredible sensitivity.

In just a few seconds, they can:

• Rewrite a sentence in a new tone or style
• Summarize complex ideas in plain language
• Help you find the words you haven’t quite found yourself

GPT-4 has around 175 billion parameters—adjustable parts of the model that tune how it processes and generates language. That’s what allows it to understand subtle context, make creative suggestions, and hold meaning across a conversation.

At their core, these models are mirrors of our language—and language is the pattern they know best.

The more clearly we speak, the more clearly they reflect.

Just like any good assistant, a language model works best when it understands the context. It doesn’t read minds—it responds to clarity. The more you share about what you need, how you think, or the tone you want, the more useful and nuanced its replies become. You’re not just giving commands—you’re creating a shared space for language to grow. That’s the art. That’s the invitation.

For example:
Instead of saying: “Help me write something.”
Try: “I’m writing a short blog post for people who are curious about using AI tools in their work. I want it to sound friendly, not too technical, and I’m hoping to include a few practical tips. Can you help me get started?”

See the difference? One is a vague request. The other sets a scene, defines a tone, and gives the model a clear purpose to work with.

This kind of clear communication is often called prompt engineering—or even context engineering. It’s not about using fancy words. It’s about being thoughtful, specific, and human. The better the prompt, the better the reflection.

If you found this helpful or inspiring, I offer guidance as an AI consultant—helping individuals and organizations understand how to work meaningfully with large language models. If you’d like to explore how this could support your work or team, feel free to reach out. We can begin with a conversation, and shape the path from there.

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#PromptEngineering
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