146 | What I *Want* Is...
Start With What You Want. Let AI Figure Out the Rest.
A two-year-old has no interest in how you're going to get them what they want. They just know they want it, and they will make that abundantly clear. It turns out that's pretty close to the right approach with today's AI tools.
Somewhere along the way, a set of rules emerged for how to talk to AI, and those rules spread through articles and workshops and LinkedIn posts until they started to feel like the only legitimate way to write a prompt. Define the role. Set the format. Specify the constraints. Establish the tone. The frameworks even got names like CRAFT, CRISPE, and a handful of others, and people learned them the way you’d learn any professional skill, with the reasonable expectation that following the steps would produce results.
The problem is that the tools those frameworks were written for have largely been replaced, and the guidance didn’t get updated when the tools changed.
What’s a Reasoning Model, and Why Does It Matter?
The first wave of AI chatbots worked by predicting the most likely next word, very quickly, without much capacity for stepping back to consider what you were actually trying to accomplish. If you wanted useful output, you had to do a lot of the thinking yourself and build it into the prompt. The frameworks were a reasonable response to that limitation.
What you’re working with today is genuinely different. ChatGPT, Claude, and Gemini now run on what are called reasoning models, or thinking models. Before generating a response, they work through the problem internally, considering different angles, checking their own logic, and figuring out what’s missing from your request. If you notice a pause of 20 or 30 seconds before an answer appears, that’s not a loading delay. The model is actually working through your problem before it starts writing.
The wait can feel odd if you’re used to instant responses, but it’s worth getting comfortable with. What comes out the other side tends to be considerably more useful.
The Shift: Destination Over Directions
The old prompting instinct made sense given what the tools needed: a detailed map with role, format, length, and tone all specified upfront.
Reasoning models don’t need the map. They need to know where you’re trying to go, and they’d prefer to figure out the route themselves.
In practice, that means starting with what you actually want rather than with instructions for how to produce it. Here’s what that looks like for a program coordinator preparing for a difficult board conversation.
The old way:
“Act as an experienced nonprofit executive director. Write three formal talking points in third-person voice addressing concerns about cash reserves.”
The model will produce three talking points. They’ll be formally written and they’ll address cash reserves, but there’s a good chance none of them will say what that coordinator actually needs to say in the room, because the prompt described a format rather than a situation.
Now try it this way:
“Our board is nervous about our cash reserves and I need to reassure them without dismissing their concern. We have four months of operating expenses on hand, which is below our policy target of six. Help me figure out what to say.”
That prompt gives the model the real situation, including the tension at the center of it. A reasoning model can work with that in a way that produces something genuinely useful rather than generically correct.
Tell It Your Problem, Not the Format
The same logic holds across the work nonprofit professionals do every day.
A development director preparing a grant renewal doesn’t need to specify bullet points, third-person voice, or formal language. She needs the model to understand which funder she’s writing for, what the program actually accomplished, and what she’s worried the funder might push back on. Give it that context and the model can help figure out the strongest argument. The formatting takes care of itself.
An HR manager drafting a sensitive staff communication doesn’t need to specify word count or reading level. She needs the model to understand who’s receiving the message, what those people are likely anxious about, and what the communication is actually trying to accomplish. Those are the inputs that produce a useful draft.
The shift is really about trusting the model to do more of the work, not because it always gets it right, but because it’s genuinely better at figuring out the how when you give it a clear picture of the what.
Context Is the Ingredient Most People Skimp On
Here’s the thing about all of this: the more the model understands about you, your organization, and your specific situation, the better it gets at reasoning toward something useful. A prompt that says “help me write a grant narrative” is going to produce something generic. A prompt that explains your organization’s mission, the funder’s priorities, your program’s outcomes, and the gap you’re worried about in your case — that’s a prompt a reasoning model can actually work with.
Most people underload their prompts on context, partly because typing it all out feels like a lot of work. If that’s what’s holding you back, it’s worth revisiting a piece from this newsletter’s “My AI Confessions” series, “I Talk More Than I Type,” which covers voice dictation tools like Wispr Flow. Talking your way through the context — your situation, your concerns, what you’ve already tried — is genuinely faster than typing it, and it tends to come out more naturally too. The context you’d give a trusted colleague out loud is often exactly the context a reasoning model needs.
There’s also a newer development worth knowing about: most of the major AI chatbots now have some form of memory, meaning they can retain information you’ve shared across different conversations. Tell Claude or ChatGPT something important about your organization once — your funding model, your target population, a strategic challenge you’re navigating — and it can factor that in the next time you open a new chat. It’s still an evolving feature and worth paying attention to, because it changes what “giving context” means. You may not need to re-explain your situation every single time.
One More Tool Worth Using
There’s a move that most people skip entirely, and it’s one of the most useful things you can do with a reasoning model: ask it to ask you questions before it responds.
Just add a line to the end of your prompt:
“Before you respond, ask me anything you need to know.”
Reasoning models are quite good at identifying what information is missing from a request. Giving them permission to surface that before they start generating an answer often produces a better result than any amount of upfront engineering. It also has a way of surfacing assumptions you didn’t realize you were making. If you’ve ever gotten a response that felt slightly off and couldn’t quite identify why, there’s a decent chance the model was filling in a gap you didn’t know was there.
A Quick Test
Take a prompt you’ve used recently and rewrite it with only three things in mind:
What outcome you’re trying to reach
What context the model needs to understand your actual situation
What a good answer would accomplish for you — not what format it should take, but what it should actually do.
Leave out the role instructions, the format specifications, and the tone guidance. See what comes back.
It runs counter to everything the frameworks trained you to do, but with reasoning models, removing that scaffolding more often than not produces a better result.
The Bigger Picture
The prompting frameworks most people learned were written for tools that needed a lot of hand-holding to stay on track, and they worked reasonably well for what those tools were. What you’re working with now is different enough that those instincts are worth revisiting.
Reasoning models work best when you give them real problems to think through rather than templates to fill in.
Context still matters, clarity still matters, and a vague request will still produce a vague response. But if you find yourself spending significant time engineering the structure of a prompt, it’s worth asking whether you’re doing work the model could do better on its own.
Tell it what you need and why it matters. Give it enough context to understand the situation. Let it figure out how to get there — and if the first answer isn’t quite right, have a conversation about it. That’s what the tool is actually built for.
Make Good Choices!