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MODULE 5 COMPLETE

⚡ Best Practices

Common errors, AI limitations, and expert tips. Accelerate your learning by avoiding common pitfalls.

10
Common Errors
⚠️
Limitations
🏆
Pro Tips
❌

1. 10 Common Beginner Mistakes

Learn from other people’s mistakes

1.

Prompts that are too vague

"Help me with marketing" → Be specific!

2.

Don't provide context

The model doesn’t know your situation—explain it!

3.

Accept the first response

Iterate! Ask for refinements and improvements.

4.

Mixing multiple tasks

One task per prompt for better results.

5.

Don't verify facts

LLMs can “hallucinate”—always verify!

🚧

2. AI Limitations

Realistic expectations prevent frustration

🌀 Hallucinations

Models may make up facts that seem real. Always verify critical information.

📅 Cutoff Date

Knowledge limited to the training date. Doesn't know recent events.

🧮 Advanced Mathematics

It can make calculation errors. Use a calculator to check.

⚖️ Bias

Reflects biases in the training data. Consider multiple perspectives.

🏆

3. Expert Tips

Years of experience in actionable tips

💡 "Take a deep breath" - Add "Think carefully before answering" for more thoughtful responses
💡 Temperature 0 - Use for deterministic tasks (code, facts)
💡 Restart when stuck - Sometimes a new conversation yields better results
💡 Save good prompts - Create a library of effective prompts
💡 Ask for self-critique - "What might be wrong with this answer?"

🎉 Congratulations! Beginner Level Complete!

You’ve mastered the fundamentals of Prompt Engineering!
Next step: Technical Level with advanced techniques.

5

🔄 Create a Review Workflow

Separate generation, critique, and the final version into visible steps.

✓ Do

Use a concrete example, specify the expected result, and record what changed.

✗ Avoid

Changing several instructions at once or accepting the first response without checking.

Practical tip

Save the version that worked and note why it worked. This record becomes your reusable standard.

6

🔐 Protect Sensitive Data

Remove personal information, secrets, and internal data before sending context to the model.

✓ Do

Use a concrete example, specify the expected result, and record what changed.

✗ Avoid

Changing several instructions at once or accepting the first response without checking.

Practical tip

Save the version that worked and note why it worked. This record becomes your reusable standard.