Most teams jump straight to prompts. They paste a topic into a tool and wait for something usable. The output might look polished, but it often lacks nuance, context, and alignment with what your business actually needs. That’s because prompts are one‑off transactions: you feed in a question, and you get a guess back. It’s a bit like asking a stranger to guess your company’s positioning based on your homepage – occasionally interesting, rarely insightful.
Real leverage doesn’t come from asking AI what to write. It comes from training AI how to think like you. Not in the sense of giving it a style sheet or a bunch of example language – but as TechTarget describes it – in teaching it the patterns, priorities, and signals that make your content behave like a strategic asset.
Prompts are surface‑level; training builds depth
A prompt is a snapshot – one request, one response. It doesn’t remember anything outside of what you type in that moment. Training, on the other hand, embeds context into the system: your brand’s voice, your audience’s language, your strategic frameworks, and the problems you’re trying to solve. It’s the difference between asking a tool to “write a blog post about X,” and having a system that understands why that topic matters, when it matters, and how it fits into your broader narrative (which Nexla describes great here, in their post about prompt engineering vs fine tuning.
Training AI means building a foundation. It means embedding your brand’s core narrative, audience insights, and format standards into a persistent knowledge base that the model can draw from. When that foundation exists, prompts become much more purposeful. Instead of starting from scratch each time, the AI builds on a shared understanding that already lives inside your system.
What training really involves
It starts with the why: your positioning, your differentiation, your audience’s pain points. Then come the how: your preferred formats, the stages of your buyer journey, and the patterns you’ve seen work in the past. Over time, this becomes a living, breathing set of signals the AI can reference – not just words it can rehash, as shown in Apple’s research on context tuning for better relevance.
When AI is trained this way, it doesn’t just generate text. It follows rules, it respects context, and it helps you generate ideas that are on strategy (and not just on topic, which is what you get with the standard "write about X").
Why prompts alone fall short
A prompt on its own can’t remember what you worked on last week. It can’t recall the phrasing you use for your ICP. It doesn’t know what’s happening in your pipeline right now. And it definitely doesn’t understand the nuances of your brand storytelling.
That’s why prompt‑only workflows feel inconsistent. You get good output sometimes, and mediocre output other times. You find yourself repeating the same context over and over in the prompt, because the tool doesn’t retain it.
Training changes that. It makes context persistent. It means the AI doesn’t just respond to your prompt, it understands it in relation to everything else you’ve taught it.
How training plays out in a real system
At Scaale we layer training into our system first. Big Brajn – our internal AI framework – is trained on the narratives, market signals, and strategic priorities that matter to our customers. When a market shift happens or a competitor narrative changes, Big Brajn doesn’t just draft a post – it contextualizes the signal within our clientsbrand strategy. It helps us – and our clients – see what’s relevant, not just write something about it.
That’s how AI becomes more than a time‑saver. It becomes a partner in the system – helping you focus on decisions instead of drafting.
The lesson for every team
If you want AI to be a multiplier and not just a machine for regurgitating language, don’t start with prompts. Start with training. Train your model on your context:
- Your voice and mission
- Your audience’s language
- Your strategic priorities
- Your existing content playbooks
Then use prompts to call that trained expertise forward. When you train first and prompt second, AI stops being a faceless text generator and starts acting like an extension of your team.
