It didn't begin with a breakthrough. It began with a rhythm — the steady cadence of conversations, the quiet unfolding of patterns. I noticed that something changed not when I used more clever prompts, but when I shaped the space around them. When the quality of the interaction deepened. When the dialogue itself became the process.
I started to realize that language is the interface — not because it contains the intelligence, but because it gives us access to it. Language is how we express the conceptual realm we're trying to build and share with the system. It's how we represent ideas, test assumptions, define goals, and explore meaning.
We don't steer AI by pulling levers or clicking buttons. We steer it through structured dialogue — through language that reflects clarity of thought.
This shift transforms how we think about our relationship with systems like HubSpot. It's about learning to use language not just to ask, but to steer. Not just to prompt, but to collaborate.
Because the quality of the conversation is the quality of the collaboration.
In traditional systems, we interact through buttons, menus, dashboards — predefined affordances that narrow our options to what the designer imagined. But with large language models integrated into business platforms, the interaction space opens up. Suddenly, the interface is a blank text box. You speak, and the system responds.
The freedom this offers is both extraordinary and destabilizing. There's no manual, no strict command language. Just you, the system, and the negotiation of meaning between your intent and its interpretation.
From a cybernetic perspective, the human-AI language interface forms a continuous feedback loop with several distinct characteristics:
This is what makes language powerful — and risky. Every ambiguity, every misplaced assumption, every omission of context becomes a steering error. Every refinement, every structure, every clarification becomes a moment of course correction.
If we return to the five-node steering model, we can now see language functioning as the control layer across every node:
Everything flows through language. The tighter the loop between expression and interpretation, the more precise the steering becomes.
The term "prompting" has become too narrow. It suggests a one-shot query, a trick, a prompt hack. But what we're doing here is different. This is dialogue-as-direction — a way of shaping what the system sees, how it thinks, and what it returns.
Prompting approach: "Write me a follow-up email for a lead who downloaded our whitepaper."
Steering approach: "Let's create a follow-up sequence for prospects who downloaded our enterprise security whitepaper. These are typically IT directors at financial institutions who are concerned about compliance. The goal isn't just to book a demo, but to position us as thought leaders who understand their regulatory challenges. Let's start with an initial follow-up that references specific insights from the whitepaper and offers additional value before suggesting next steps."
When we treat language as steering, the goal isn't just a better response. It's a better relationship. A shared model of meaning. A loop that improves with every exchange.
This is the difference between prompting an assistant and partnering with a co-pilot.
To steer well, language must carry more than just a request. It must carry:
In the absence of these elements, the system fills in the blanks — often incorrectly. Steering means removing ambiguity where it matters, and allowing openness where exploration is useful.
The most powerful linguistic collaboration typically emerges not from single exchanges but from ongoing dialogue—a continuous conversation that adapts and evolves over time. This iterative approach embodies the essence of adaptive steering, with each exchange providing feedback that refines subsequent communication.
Effective collaboration often follows what we might call a dialogue spiral—a progressive refinement where each exchange builds on previous understanding:
In practice, the dialogue spiral transforms vague initial questions into precise explorations of specific challenges.
What begins as a general interest in improving customer journeys evolves, through conversation, into targeted analysis of specific friction points. The dialogue naturally progresses from broad concepts to nuanced examination of particular elements—like email confirmation processes or onboarding sequences—where meaningful improvements can be made.
This progressive refinement happens not through artificial examples, but through the natural evolution of thought as human and AI build shared understanding. Each turn in the conversation adds specificity, context, and focus.
This is a cybernetic loop — perception, feedback, adjustment. Language is what makes it adaptive. Every exchange teaches the system something — and teaches you something about the system.
The longer the dialogue, the more nuanced the collaboration becomes. This is where fluency lives — not in writing the perfect prompt, but in navigating uncertainty through shared language.
The metaphors we use to conceptualize AI co-pilots fundamentally shape how we interact with them. These metaphorical frameworks are not merely linguistic flourishes but cognitive tools that structure our understanding of these systems and influence our expectations.
Three types of metaphorical frameworks are particularly influential:
Structural Metaphors map organized knowledge from one domain onto another:
Orientational Metaphors organize concepts in spatial relationships:
Ontological Metaphors let us conceptualize abstract AI capabilities as concrete entities:
The metaphors we choose should align with our intended relationship and specific contexts. For effective HubSpot implementations, consider metaphors that highlight complementary strengths and accommodate evolution of the relationship over time.
AI systems lack the contextual awareness that humans develop through lived experience and organizational immersion. This contextual deficit must be actively addressed through deliberate information sharing.
When working with AI co-pilots in HubSpot environments, certain contextual elements prove particularly valuable:
Ambiguity in language serves both productive and problematic functions:
Productive Ambiguity: Sometimes, openness in language creates space for creativity. Example: "Explore innovative approaches to customer engagement" allows for wider-ranging ideation.
Problematic Ambiguity: Unintended ambiguity creates misalignment and wasted effort. Example: "Improve our sales process" could be interpreted in countless ways.
The key skill is learning to use ambiguity strategically while eliminating it where precision is needed.
Different steering challenges require different linguistic approaches. Recognizing which communication patterns best suit particular scenarios enables more efficient collaboration.
When steering through uncertain or ambiguous situations, communication patterns that emphasize breadth of consideration prove most effective:
For scenarios where the destination is clear but the path requires careful navigation:
For environments characterized by rapid change and emergent conditions:
Because the better you communicate, the more capable the system becomes. Because language is the one interface we all share. Because in this era, your clarity is your advantage.
When we treat language as interface — not just expression, but structure, system, and steering — we open the door to something genuinely new: not just better technology, but a better kind of collaboration.
The steering challenges of our complex world increasingly exceed the cognitive capacity of any individual, regardless of their intelligence or expertise. By learning to effectively collaborate with AI through language, we expand our navigational capabilities, enabling us to steer more effectively through domains that might otherwise exceed our individual capacity.
In this sense, linguistic mastery becomes not just a technical skill but a fundamentally strategic capability for effective steering in the modern world.
And that's the art of steering.