What are common prompt engineering mistakes?

The 7 Most Costly Prompt Engineering Mistakes

These mistakes cost time, money, and frustration. Here's how to identify and fix them:

Mistake #1: Being Too Vague

❌ Wrong:

Write about social media marketing

Problem: No specific goal, audience, or format defined

✅ Fixed:

Write a 500-word LinkedIn article about 3 social media marketing strategies 
for B2B SaaS companies. Include specific examples, actionable tips, and end 
with a clear call-to-action. Target marketing managers with 2-5 years experience.

Solution: Specific length, audience, format, and requirements

Mistake #2: Asking Multiple Questions at Once

❌ Wrong:

Explain machine learning, how it differs from AI, what are the best tools, 
and create a learning roadmap for beginners

Problem: Four different requests competing for attention

✅ Fixed:

Create a 6-month machine learning roadmap for beginners with programming 
experience. Include specific resources, time estimates, and milestone projects 
for each month.

Solution: One focused request with clear deliverable

Mistake #3: No Context or Background

❌ Wrong:

Write a proposal for the Johnson project

Problem: AI has no idea what the Johnson project is

✅ Fixed:

Write a project proposal for Johnson Manufacturing's website redesign. They're 
a 50-employee industrial equipment company needing mobile optimization, modern 
design, and lead generation improvements. Budget: $25k, timeline: 3 months.

Solution: Complete context about client, project, and constraints

Mistake #4: No Output Format Specified

❌ Wrong:

Analyze our customer feedback data

Problem: No guidance on format, structure, or detail level

✅ Fixed:

Analyze our customer feedback data and create a summary report with:
1. Top 5 positive themes with frequency percentages
2. Top 5 issues with severity ratings
3. 3 actionable recommendations with implementation difficulty
4. One-paragraph executive summary

Solution: Exact format and structure requirements

Mistake #5: Inconsistent Terminology

Problem: Using different terms for the same concept confuses the AI and leads to inconsistent responses.

❌ Inconsistent:

customers, consumers, users, buyers (all referring to the same group)

✅ Consistent:

Choose one term and stick with it throughout the prompt

Mistake #6: Not Testing Before Deploying

Problem: Using prompts in production without testing leads to poor results and wasted resources.

Quick Testing Protocol:

1. Run the prompt 3 times with the same input
2. Test with different types of input data
3. Check edge cases and unusual scenarios
4. Verify output format consistency
5. Measure time and token usage

Mistake #7: Ignoring Token Limits and Costs

Problem: Long, inefficient prompts waste money and can hit token limits.

❌ Inefficient:

Repetitive instructions, unnecessary background, verbose examples

✅ Efficient:

Concise instructions, only relevant context, clear examples

Prompt Quality Diagnostic Checklist

Use this checklist to evaluate your prompts before deployment:

Content Quality

• ✅ Specific, actionable instructions
• ✅ Sufficient context provided
• ✅ Clear success criteria
• ✅ Consistent terminology

Structure & Format

• ✅ Single, focused objective
• ✅ Desired output format specified
• ✅ Appropriate length guidance
• ✅ Examples provided when needed

Technical Optimization

• ✅ Token-efficient language
• ✅ No contradictory instructions
• ✅ Tested multiple times
• ✅ Edge cases considered

Recovery Strategies for Bad Prompts

When Your Prompt Isn't Working:

1. Add Specificity

   • Define exact requirements
   • Specify measurements (word count, list items, etc.)
   • Include concrete examples

2. Simplify

   • Break complex prompts into multiple steps
   • Focus on one task at a time
   • Remove unnecessary context

3. Provide Examples

   • Show desired input-output pairs
   • Demonstrate format expectations
   • Illustrate style preferences

4. Iterate Systematically

   • Change one variable at a time
   • Document what works and what doesn't
   • Build a prompt library of successful patterns

Before & After: Real-World Fixes

Email Marketing Campaign

❌ Before:

Write promotional emails for our sale

✅ After:

Create 3 promotional email variations for our 48-hour summer sale:
- Audience: Existing customers who purchased in last 6 months
- Discount: 30% off all items
- Tone: Urgent but friendly
- Length: 150-200 words each
- Include: Subject line, preview text, main CTA
- Format: HTML-friendly structure

Data Analysis Request

❌ Before:

Look at these numbers and tell me what you think

✅ After:

Analyze this Q4 sales data (attached) and provide:
1. Month-over-month growth percentages
2. Top 3 performing product categories
3. 2 concerning trends with supporting data
4. 3 specific actionable recommendations
Format as executive briefing: 1-page maximum, bullet points, visual data markers

Common Anti-Patterns to Avoid

The "Everything" Prompt

Trying to get everything done in one massive prompt instead of breaking tasks into logical steps.

The "Assumption" Prompt

Assuming the AI knows your context, industry jargon, or specific requirements without stating them.

The "Contradictory" Prompt

Giving instructions that conflict with each other (e.g., "be brief but comprehensive").

The "Untested" Prompt

Using prompts in production without running test iterations to verify consistency.

Frequently Asked Questions

How do I know if my prompt is too vague?

If you get inconsistent results across multiple runs, or if the AI asks clarifying questions, your prompt is too vague. Add specific requirements, examples, and constraints. A good test: Could another human complete the task with the same instructions?

Should I include examples in every prompt?

Not always. Include examples when: you need a specific format, the task is unusual or complex, you want to demonstrate style/tone, or you're getting inconsistent results. Skip examples for straightforward tasks with clear instructions.

How many times should I test a prompt?

Run it at least 3 times with identical input to check consistency. Then test with 3-5 different inputs representing typical use cases. For production prompts, test 10+ times with diverse inputs including edge cases.

Related Topics

• How to Create Effective AI Prompts
• Prompt Engineering Best Practices
• What is Prompt Engineering?