Changing Your Mind Is Ok: Context Training, Explained

Changing your mind is allowed

I'll be honest with you, because that's what this newsletter has always been for. I was in a season of deciding what I actually wanted.

I had built a lot. Building it showed me something I couldn't have learned from thinking about it: I had more fun doing the strategy and creating the system than managing day-to-day implementation and support.

I thrive in strategy and execution. Self-sustaining systems. Deep discovery. One-to-one conversations. Teaching and sharing what I learn.

I'm a front loader. I love the ideas. I love showing you the art of the possible.

Doing something well isn't the same as enjoying it. Building something isn't a lifetime agreement to keep building it in the same way.

It's okay to launch something and change your mind, even when it took real time to build.

Sunk cost doesn't become direction just because you worked hard. The useful question is what the action taught you and what the evidence says to do next.

The consistency is the part that compounds. Direction can change while the deeper work becomes clearer.

For me, the deeper work was Context Training.

Why two people with the same AI get different results

Two people can open the exact same AI tool with the same subscription and the same model. One gets work that sounds like a stranger wrote it: polished, generic, and a little off. The other gets work that sounds like them, uses their real information, and reflects the world the work belongs to.

The difference isn't only the tool, and it isn't a clever prompt. It is context.

Context Training is teaching AI what it needs to know to do the job you're asking of it, aligned to what you actually need and refined as you go.

Think about it like a hire. If you brought on the most brilliant person you've ever met and gave them zero onboarding, no context about who you serve, how you talk, or what you sell, their first week would be generic too. Not because they aren't sharp, but because they don't know you yet.

Generic AI output is often an onboarding problem. Almost nobody treats it that way.

There are four things your AI needs to know to stop guessing:

1. Who you help. The real person, audience, customer, team, or community the work serves.
2. How you sound. Your actual language, rhythm, preferences, and examples, not a list of vague adjectives.
3. What you sell or deliver. The current offer, service, product, role, or outcome.
4. What's true right now. The facts, priorities, constraints, and decisions that may have changed since the last assignment.

When those live in one place the AI reads before it starts, the guessing decreases.

The data science version is simple: your context is a dataset. Curate it well, keep it current, and store it somewhere you control. Then it can move with you when the tool changes.

One thing to do today

Pick one page you need for something you know well: an event, newsletter, resource, workshop, or idea.

Give the AI the four kinds of context above before you ask it to build. Talk through what you're trying to do. Show it an example of your voice. Tell it what is true today. Then review the result the way you'd review work from a new teammate.

Don't only fix the page. Save the correction that made the page better, so the next assignment starts smarter.

If this is your kind of conversation, join me for the next Seed & Society® newsletter.