I'm going to describe a scene, and I want you to tell me if it's familiar.
You've decided to take AI seriously. You've signed up for ChatGPT or Claude or both. You're genuinely curious and a little excited. You open the interface, and you type something like: "Help me grow my business."
The AI responds with a list. Seven things, or ten things, or five steps. The list is reasonable. It's well-organized. It addresses several real dimensions of business growth. You read it, you nod, you maybe copy-paste it into a document.
And then nothing happens. Not because the list was bad. Because a list of reasonable things is not the same as an actionable plan for your specific situation, and you've just experienced AI at its most seductive and least useful — broad, confident, plausible, and completely disconnected from the specifics that would make it actually helpful.
That's the mistake. Let me unpack why it happens and what to do instead.
The "expert in everything" trap.
Large language models have been trained on an enormous amount of human knowledge and writing. They can produce credible-sounding output on almost any topic. This is the feature everyone talks about.
What gets talked about less is the limitation that comes with it: because these models have been trained to produce plausible responses to almost any query, they will produce a plausible response even when "I don't have enough information to give you a useful answer" would be the more honest reply.
When you ask an AI to help you grow your business without telling it anything about your business, it responds with generic business growth advice. Not because generic advice is what you need — it almost certainly isn't — but because generic advice is what fits the question you asked. The model is giving you the best response it can to the information it has. The problem is the information it has is insufficient.
This is not a flaw in the AI. It's a mismatch between how people approach AI (as a magic advice machine) and how it actually works (as a very capable tool that responds to what you give it).
What everyone should do in week one instead.
The unlock for AI usefulness is specificity. Not just specific questions — specific context.
Before asking for help with a task, tell the AI:
- Who you are and what you're trying to accomplish
- What constraints you're working within
- What you've already tried
- What a good outcome looks like to you
This is not complicated. It's just the information you'd give a smart human consultant if you were paying them $400 an hour and wanted your money's worth.
The reason this doesn't happen naturally is that AI interfaces look like search boxes, and we've been trained by Google to ask questions in three to five words. AI is not a search engine. It's closer to a conversation with a knowledgeable collaborator who has excellent recall and zero context about you until you provide it.
The feedback loop problem.
The second mistake, closely related, is treating AI outputs as endpoints rather than starting points.
When the AI gives you a draft or a plan or an analysis, that's the beginning of the work, not the end. The output needs to be evaluated against your knowledge of the situation, pushed back on, refined. "This is pretty good" followed by publishing it or acting on it without revision is where the damage happens — not because AI outputs are bad, but because first drafts are first drafts, regardless of who or what produced them.
The people who get the most out of AI are the ones who approach it as a dialogue. They get an output. They react to it. They tell the AI what's wrong with it and why. They iterate. The quality improves through cycles of engagement rather than through a single perfect prompt.
This takes more time than treating AI as an instant answer machine. It also produces results that are actually useful.
The practical exercise for week one.
Take something you're genuinely working on — a real decision, a real piece of writing, a real problem. Write the most complete context you can about it: who's involved, what you've tried, what you want to achieve, what "good" looks like. Then ask for help.
Compare that output to what you'd get from a three-word query on the same topic.
The difference will tell you everything you need to know about why specificity is the skill that matters more than any particular tool.
This Week in AI: Anthropic released research recently on what they're calling "Constitutional AI" principles — the frameworks used to align AI behavior with intended values. The technical details are less important than the practical implication: the guardrails in current AI models are a design choice, not a technical limitation. Understanding this helps you work with the models more effectively and understand why they respond the way they do to certain kinds of requests.
The Prompt Playbook in our free toolkit goes deep on this — real examples, common failure patterns, and the exercises that accelerate the learning curve. Free at start.tenstreamslab.com.