# Asking better questions

> The meta-skill behind every good AI session. What to do when the first answer is generic, and how to push past it

_12 min · beginner · track: tips-and-tricks · id: asking-better-questions_

> **Team:** 
>
> Most "the AI is bad at this" moments are actually "the question was too
> vague" moments. This course is the handful of moves that fix it: add
> constraints, name the audience, paste an example, ask for options instead
> of an answer.

You have already met the principle ([Context is king](/course/principles)) and the four-part frame from [Chat agents](/course/chat-agents). This course is the next layer down: what to do when the first answer comes back generic, and how to get unstuck without giving up on the session.

## The first answer is rarely the best

**A generic answer is almost always a context problem, not a model problem.** When the first reply is too vague, too obvious, or just slightly off, the right move is not to retry the same prompt or switch to a more capable model. It is to add what was missing. The model is doing its best to read your mind; the fix is to stop making it guess.

The pattern is simple: you write a prompt, the answer is not great, and instead of typing a follow-up complaint ("be more specific"), you write a sharper version of the original. Pasting the previous bad answer back in works too: "the version below is too generic, here is what is missing".

## Five moves that push a generic answer into a useful one

**Five small adjustments cover most of the cases where the first answer falls flat.**

- **Add the constraints.** "in three sentences", "for a senior engineer", "without using the word X", "by Friday". Constraints turn a wide question into a narrow one.
- **Add the audience.** "Explain this to a junior PM" lands very differently from "explain this to me". The audience is half the prompt.
- **Add what you tried.** "I have already considered X and Y, here is why neither worked." Moves the conversation past the obvious answers in one turn.
- **Ask for options instead of an answer.** "Give me three different ways this could go, with the trade-offs." Lets you pick instead of accepting whatever the model produced first.
- **Show, do not tell.** Paste an example of what good looks like. "Here is a status update I wrote last week and liked. Match that tone."

> **Tip:** 
>
> **Try it.** Take a prompt from your last session that did not produce a useful answer. Apply two of the five moves above, re-ask, and compare.

## When to switch tools, when to stay

**Most "the model is bad at this" complaints turn out to be a question problem.** Before you switch tools, check whether you tried any of the five moves above. If you did and it still falls apart, then a different agent might fit better: Claude for long careful work, Gemini for data and Workspace context, NotebookLM when you need everything grounded in your own sources. But the order matters: tighten the question first, switch tools second.

A useful test: rewrite the prompt as if you were paying a colleague to do the work. If the rewritten prompt would let a real human do a good job, the agent will probably handle it. If a colleague would still be confused, switching tools will not unconfuse the model either.

## Hands-on

1. Open the chat history of whichever agent you use most. Find a prompt from the last week where the answer was not great. Without changing tools, rewrite the prompt by adding two of: constraints, audience, what you tried, "give me three options", an example. Re-ask.

2. Pick a question you want a real answer to today. Before sending, ask the agent: "what context do you need from me to give a good answer to this?". Read what it asks for, paste in the missing pieces, and send the real prompt only after.

3. Take a recent ambiguous brief from your work (a Slack thread, an email, a doc). Ask the agent to "list five different ways I could interpret this brief, with the trade-offs of each". Pick the one that matches what you actually meant, and use that as the basis for the next question.

## Reflect

- Which of the five moves do you already do without thinking, and which feels least natural? The one you avoid is usually the one that would help most.
- When you get a generic answer from the model, what is your first instinct: switch tools, retry the prompt, or rewrite the question? The third one is the right one. How often do you actually do it?