Your prompt is probably worse than you think.

I’ve been writing code for 30 years, and recently I built an entire video clipping application using mostly Claude and AI coding assistants. Some days I’d knock out a feature in two prompts and feel like a genius. Other days I’d burn an hour rephrasing the same request over and over, getting increasingly frustrated.

The difference wasn’t the AI. It was me.

The problem is there’s no feedback loop. You either get what you want or you don’t, and then you move on. Unlike code—where tests fail, linters complain, and type systems catch your mistakes—prompts give you no signal about why they worked or didn’t.

So I started studying what separated my good prompts from my bad ones. After analyzing hundreds of my own prompts and building a system to evaluate them, I’ve identified five dimensions that determine whether a prompt will get you what you want.

The 5 Dimensions of Prompt Quality

1. Clarity

A clear prompt is well-structured and easy to understand. It uses precise language, avoids ambiguity, and organizes information logically.

Bad prompt:

make it work better and fix the thing with the layout

Good prompt:

The sidebar navigation is overlapping with the main content area 
on screens narrower than 768px. Please adjust the CSS so the 
sidebar collapses to a hamburger menu on mobile viewports.

The first prompt requires the AI to guess what “it” refers to, what “better” means, and which “thing” you’re talking about. The second prompt leaves nothing to interpretation.

Signs of poor clarity:

  • Vague pronouns (“it,” “that,” “the thing”)
  • Multiple possible interpretations
  • Stream-of-consciousness structure
  • Missing punctuation or formatting

2. Specificity

Specificity means providing sufficient detail about requirements, constraints, and expected outcomes. This includes naming technologies, defining behaviors, and setting boundaries.

Bad prompt:

create a login form

Good prompt:

Create a login form component in React with TypeScript. Include:
- Email and password fields with validation
- Show/hide password toggle
- "Remember me" checkbox
- Submit button that disables while loading
- Error message display for failed attempts
Use Tailwind CSS for styling with a clean, minimal design.

The first prompt will get you a login form. The second prompt will get you your login form.

Specificity includes:

  • Technology stack (React, Vue, Python, etc.)
  • Libraries and frameworks (Tailwind, Prisma, etc.)
  • Behavioral requirements (what happens when…)
  • Visual requirements (styling approach, design system)
  • Edge cases (error states, loading states, empty states)

3. Context

Context means including the necessary background information and constraints that the AI needs to give you a relevant solution.

Bad prompt:

add user authentication

Good prompt:

Add user authentication to our Next.js 14 app. We're using:
- Prisma with PostgreSQL for the database
- Server Actions for mutations
- An existing User model with email and hashedPassword fields

We want email/password auth only (no OAuth for now). 
Sessions should persist for 7 days.

Without context, the AI will make assumptions. Sometimes those assumptions are fine. Often they’re not, and you end up with a solution that doesn’t fit your existing codebase.

Context includes:

  • Existing tech stack
  • Project constraints
  • What already exists
  • What you’ve already tried
  • Performance or security requirements

4. Efficiency

Efficiency is about being concise yet comprehensive. A good prompt includes everything necessary but nothing extraneous. It’s not about being short—it’s about having a high signal-to-noise ratio.

Inefficient prompt:

So I've been working on this project for a while and we have 
this dashboard and I was thinking maybe we could add some 
charts to it? Like the kind that show data over time? We use 
React by the way. And TypeScript. What do you think would be 
the best approach? Maybe something with lines?

Efficient prompt:

Add a line chart to our React/TypeScript dashboard showing 
daily active users over the past 30 days. Use Recharts. 
The data comes from our /api/analytics endpoint which returns 
{ date: string, activeUsers: number }[].

The first prompt has lots of words but low information density. The second prompt is half the length but tells the AI exactly what you need.

Signs of inefficiency:

  • Unnecessary preamble or backstory
  • Hedging language (“maybe,” “I think,” “what do you think”)
  • Redundant information
  • Thinking out loud instead of stating requirements

5. Problem-Solution Fit

This dimension measures whether your prompt actually addresses the challenge at hand. It’s possible to write a clear, specific, contextual, efficient prompt that completely misses the point.

Poor problem-solution fit:

Prompt: "Optimize this SQL query for performance"
Actual problem: The page is slow because you're making 
50 separate API calls in a loop

Good problem-solution fit:

The user list page takes 8 seconds to load. I suspect it's 
because we're fetching user details individually in a loop. 
Here's the current code: [code]. How can we reduce this to 
fewer database calls?

Before prompting, ask yourself: Am I solving the right problem?

Signs of poor problem-solution fit:

  • Asking for a specific solution when you should describe the problem
  • Solving a symptom instead of the cause
  • Scope mismatch (asking for too much or too little)

How the Dimensions Work Together

These dimensions aren’t independent. A prompt can be crystal clear but lack specificity. It can be highly specific but missing crucial context. The best prompts score well across all five.

Here’s an example that puts it all together:

Create a React component for a todo list with these requirements:

Technical:
- React 18 with TypeScript
- Tailwind CSS for styling
- Zustand for state management

Features:
- Add new todos via text input + Enter key
- Toggle completion status (strikethrough when done)
- Delete individual todos with confirmation
- Filter view: All / Active / Completed
- Persist to localStorage, rehydrate on mount

UI:
- Clean, minimal design
- Smooth transitions for state changes
- Mobile-responsive
- Empty state message when no todos

Please provide the component and any supporting files 
(store, types) as separate code blocks.

This prompt has:

  • Clarity: Well-organized with clear sections
  • Specificity: Named technologies, explicit features
  • Context: Technical constraints are clear
  • Efficiency: No wasted words, high information density
  • Problem-solution fit: The ask matches a well-scoped component

Evaluating Your Own Prompts

Before you hit send, run through this quick checklist:

  1. Clarity: Could someone else read this and understand exactly what I want?
  2. Specificity: Have I named the technologies and defined the behaviors?
  3. Context: Does the AI have the background info it needs?
  4. Efficiency: Can I remove words without losing meaning?
  5. Problem-solution fit: Am I asking for the right thing?

If you’re unsure about any of these, your prompt probably needs work.

The Payoff

When you write better prompts, you get:

  • Fewer back-and-forth iterations
  • Less “that’s not what I meant” frustration
  • More usable code on the first try
  • Faster shipping

The developers who master this skill will have a significant advantage. They’ll ship features while others are still clarifying requirements.

It’s a skill worth practicing deliberately.

Try It Yourself

Curious how your prompts score across these 5 dimensions?

VibeQ’s free evaluator analyzes your prompts instantly and shows you exactly where to improve.

Evaluate Your Prompt →

Or if you want structured practice, try the daily coding challenges where you solve problems by prompting instead of writing code.

Take Today’s Challenge →