If you’ve ever felt disappointed by an AI response, here’s the truth: it probably wasn’t the model’s fault. It was the prompt.
Good outputs come from good inputs. And most people unknowingly make the same seven mistakes when they talk to AI. The good news? They’re all fixable.
Let’s break them down.
1. No Context
The problem:
You tell the AI to “analyse this” — but it doesn’t know who you are, who the output is for, or why it matters. When the model has to guess your role, audience, or constraints, errors multiply fast.
The Fix:
Start every prompt with context. Tell the AI who it is, who the output serves, and what the boundaries are.
Example:
“You are a product analyst. Analyse this transcript for early-stage founders to identify vision outliers. Output a 5-bullet decision memo, maximum 180 words.”
2. Vague instructions
The problem:
“Write about marketing trends.” Okay… but what does success actually look like? Without a clear goal, you’ll get bland, generic outputs that don’t help anyone.
The fix:
Define what success looks like. Be specific about length, structure, and deliverables.
Example:
“Write a 1,000-word brief on the three most important B2B AI marketing trends for Q3 2025. Include one data point per trend with a source, and add a one-line implication for each.”
3. Treating it like Google
The problem:
Asking questions is level 1. AI is a partner, not a search engine. When you only ask questions, you leave all the creative work to guesswork.
The fix:
Give directives. Tell the AI what to do, not just what you want to know.
Example:
“Draft a 5-step onboarding flow for a B2B SaaS product. Include email subjects, timing in days, and one KPI to track per step.”
4. Asking for everything at once
The problem:
“Create our go-to-market plan, website copy, and investor memo.” Big prompts hide small failures. You’ll end up with sprawling, unfocused outputs that miss the mark.
The fix:
Break big asks into small, sequential steps. Build complexity gradually.
Example:
- Step 1: “List five customer jobs-to-be-done with one-line pain points.”
- Step 2: “Using JTBD 2 and 4, write five homepage headline options.”
- Step 3: “Expand headline #3 into a 150-word hero section.”
5. Not iterating
The problem:
It’s called ChatGPT for a reason. You’re supposed to have a conversation. Too many people treat it like a one-shot tool and get frustrated when the first output isn’t perfect.
The fix:
Build step-by-step. Refine as you go. Iteration beats length every time.
Example:
- Step 1: “List five potential angles for my article on remote work.”
- Step 2: “Using angle #2, write 10 SEO-friendly title options.”
- Step 3: “Create a full outline for title #7.”
6. No format or tone
The problem:
“Write the article.” Without guidance, AI defaults to a safe, dull structure and a generic voice that sounds like every other piece of content on the Internet.
The fix:
Set the format and the tone. Be explicit about structure, length, and voice.
Example:
“Write a LinkedIn post, 220 words. Structure: Hook (2 lines), 3 bullets, 1 CTA. Tone: direct, practical, plain English. Avoid jargon.”
7. No examples
The problem:
AI learns your taste through examples. Without examples, there is no style alignment. You’ll get something functional, but it won’t sound like you.
The fix:
Show the AI what “good” looks like. Paste examples of tone, structure, or style you want to match. Include anti-examples too.
Example:
“Model the tone and density on these two snippets [paste]. Avoid this anti-example [paste]. Keep sentences under 16 words.”
The R-E-X prompt: A shortcut that works
If you only remember one thing from this post, make it this:
☑ Role — Tell the model who it is
☑ Expectation — Define what success looks like
☑ Xample — Show what “good” means
This three-part formula works for 99% of prompts. It’s simple, repeatable, and effective.
The bottomlLine
→ Good prompts are not wishes.
→ Good prompts are clear briefs.
→ Good prompts get good outputs.
→ And iteration always beats length.
If you want to improve your prompting, stop asking questions and start collaborating. Treat your AI like a creative partner, not a vending machine.
The difference between mediocre and great output isn’t the model. It’s the prompt.
