Before you can use AI well, you need a mental model for what it actually is — not the sci-fi version, not the hype version. AI is a pattern-completion engine. It has processed an enormous amount of human text, and when you give it instructions, it produces the most useful response it can based on what it's learned.
That means it's incredibly good at tasks that involve language: summarizing, drafting, explaining, restructuring, translating ideas. It's less reliable when it needs to verify real-time facts or perform complex calculations without help.
The good news: you don't need to understand any of the technology underneath. You just need to understand the relationship — you're the expert on your situation, AI is the expert on language. Together you're more powerful than either alone.
Key insight: AI doesn't "know" things the way you do. It produces responses based on patterns. Your job is to give it enough context that those patterns produce something genuinely useful for you.
Not every task benefits from AI equally. The fastest wins come from identifying work you do that is repetitive, language-heavy, and time-consuming — but doesn't require your unique judgment to initiate.
Think about tasks you do every week: drafting emails, summarizing documents, writing reports, explaining things to others, researching topics, structuring ideas. These are your starting points.
The goal at this stage is simple: make a list of five tasks in your work or daily life that fit this pattern. You don't need to change anything yet — just see where the opportunity is.
Key insight: The best first AI tasks are ones where a "pretty good" first draft saves you significant time — even if you edit it heavily afterward. Done is better than perfect when the alternative is starting from nothing.
A prompt is just an instruction. But the quality of your instruction determines the quality of the output. Most people write vague prompts and get vague results — then blame the AI.
The Plainly prompting method has four parts, remembered as RCTF: Role (tell AI who it's being), Context (give it relevant background), Task (say exactly what you need), Format (specify how you want the output). You don't always need all four — but the more you include, the better the result.
Key insight: Treat AI like a brilliant assistant on their first day. They're capable — but they need context. The more you explain your situation and what "good" looks like, the better they perform.
Most people try a prompt once, don't love the result, and give up. That's the wrong approach. The first output from AI is a starting point, not a final answer. The real power comes from the conversation that follows.
Think of it like editing with a collaborator. You can say: "Make this shorter." "Rewrite the second paragraph to be more direct." "Give me three alternative versions of the opening." "Now make it sound less formal." AI responds to all of these — immediately, without complaint.
The skill of refinement is learning how to diagnose what's wrong with an output and translate that into a clear instruction. This improves quickly with practice.
Key insight: A conversation with AI is like an editing session. Each message is a revision instruction. The quality of your final output depends on how well you guide the process — not just the first prompt.
Once you've found prompts that work, save them. A prompt that produces great results once will produce great results every time you use it with similar inputs. This is how you go from "I used AI once" to "AI is part of how I work."
A workflow is a sequence: a trigger (something happens), an AI step (you apply your prompt), and an output (something useful is produced). The simplest workflows are just one step. More advanced ones chain multiple prompts together.
Start with one workflow from your Stage 2 list. Run it for two weeks. Refine it. Then add a second. Within a month, you'll have a personal AI system built around your actual life.
Key insight: The goal isn't to use AI for everything. It's to identify the 20% of your tasks where AI saves 80% of the effort — and systematize those. That's where the real gain lives.
You now have a mental model for AI, a method for prompting it, and a path to building workflows that save real time. The next step is putting it into practice — starting with one task, this week.