Context-aware AI explained: why your AI keeps forgetting who you are

I use AI every day, for almost everything. This summer I caught myself yelling at it. I had explained the same thing 50 times, a million times, and it still came back with something that had nothing to do with us.

That moment is the whole case for context-aware AI. Context-aware AI reads your company's own context before it writes or prepares anything. It knows your brand voice, your products, your customers, the decisions you already made and what needs a human approval. Generic AI starts from zero in every chat. So somebody has to carry the whole company in their head and type it in again, every single time.

Usually that somebody is the founder or the marketing lead.

Why generic AI fails at business work

Most marketing teams already use AI. They just use it in ten different tools. The idea gets spun in ChatGPT, the research happens by hand on a few social profiles, then somebody writes the post, asks ChatGPT for an image prompt and pastes it into the next tool. For video it's even worse.

Every one of those tools needs to hear the story again. Who you are, what you do, what your colors are. It costs time, and the results are still bad, because the AI only knows what the person remembered to paste in that day.

Since early this year my view has been that AI at work gets stuck for three reasons. Either the software isn't connected, compliance gets in the way, or the AI doesn't have enough context to give a good result. The third one is the one you feel every day, because it shows up in every draft.

And you've seen what comes out. Someone prompted "write me a LinkedIn article about XYZ", you read two lines and think, man, I don't want to read that. Social media is full of it. On top of that, AI always produces too much text. Nobody reads it, and then mistakes happen.

What context-aware AI does differently

A better prompt helps for one chat. Tomorrow you start from zero again.

Context-aware AI keeps the company in one place and reads it before every piece of work. Brand voice, products, who your customers are, what you decided last month, which claims a person has to check. The marketing lead doesn't have to be the memory of the company anymore. They review.

The review part matters. Most of what AI produces fully on its own is super generic and, to be honest, pretty shitty. So in our setup the AI prepares and a human approves before anything goes out. The system does the work you don't want to do yourself. You click and check.

How I found my own voice (in my calls)

For a long time I got this wrong.

Writing with AI made me really unhappy, because everything became so AI. We gave it a written profile of how I write, and the posts still sounded AI.

What changed it was using my own words from my calls. When the AI writes from the way I talk, it knows my background, the crypto years, the DAO stuff. Suddenly the output got much better. Partly because at least I like how I speak.

It took a long, long time to find that way. Now I get a draft I like and I don't edit it for hours.

For me that's the clearest proof of what context does. Same models, same person. The only difference is what the AI knew about me before it started writing.

What this looks like in our own marketing

Since October our own content engine at Haba runs every Thursday. It reads what happened in the company that week, writes next week's LinkedIn posts and a blog post, checks them against my writing rules, and puts everything on one approval page.

I go through the whole week in one session. What I send back gets rewritten, and the reason goes into a learnings file the engine reads before it writes again. So the context grows every time I say no. Nobody pastes the brand voice into a chat window, because it's already there.

We now offer the same engine to marketing teams at tech, Web3 and AI companies as a 30-day pilot, with the first finished work in week one.

If you run marketing at a tech company and want to see what our engine produces in a week, get in touch.