Chapter 2 · Lesson 1
Vague In, Vague Out
See why vague prompts create vague answers and how to fix them.
You ask AI to "make a plan" and get something bland enough to ignore.
AI fills gaps with averages
When your request lacks context, the model guesses the most common version of the task. That usually produces safe, generic work.
Give the missing edges
Add audience, goal, constraints, format, and examples of what good looks like. Those details narrow the answer into something useful.
Build the mental model
A language model cannot see the goal in your head. It responds to the evidence in your message, then fills every missing decision with a plausible default.
Every gap becomes a guess
If you do not name the audience, AI imagines a broad one. If you do not set a goal, it chooses a common one. If you do not define limits, it optimises for a safe, average answer.
Specific does not mean long
A useful prompt can still be one sentence. The important part is including the few facts that materially change the answer: who it is for, what success means, and what must fit.
A solo app founder needs a launch plan they can actually complete.
Make me a launch plan for my app.
Create a seven-day launch checklist for a solo iOS app founder. I have 30 minutes per day, no advertising budget, and an existing LinkedIn audience. Give me one concrete action per day in a table.
The improved version fixes four decisions: timeframe, available effort, useful channels, and output format. AI can now prioritise instead of producing a generic list of marketing tactics.
Why missing context produces an average answer
AI generates likely continuations from patterns learned across large amounts of text. With a broad request such as “make a plan”, many different plans could be reasonable. The response therefore tends toward familiar, widely applicable advice.
Context changes which patterns are relevant. Constraints are especially useful because they remove attractive but impossible options. You are not teaching the model a secret formula; you are reducing ambiguity in the assignment.
Make one real request less vague
Choose a task you may do this week. Add one audience, one outcome, one real constraint, and the format you want back.
Help me [task] for [audience]. The goal is [outcome]. Keep within [constraint]. Return it as [format].prompt choice
Which prompt is least likely to produce a generic answer?
Compare the prompts closely. Look for useful context, constraints, and a clear output.
Specific inputs create specific outputs.