Tips and Common Pitfalls in Prompt Engineering

⚠️ Common Pitfalls in Prompt Engineering

Achieving success in prompt engineering requires awareness of frequent mistakes that can impact AI response quality:

🚫 Pitfalls to Avoid

• ❓ Overly vague prompts → Lead to irrelevant or broad outputs
• 📏 Excessively long prompts → May confuse the model or hit token limits
• 🔄 Ambiguous or contradictory instructions → Hinder clarity and coherence
• 🌐 Ignoring context → Produces generic, shallow answers

✅ Best Practices to Overcome Pitfalls

• ✍️ Aim for concise clarity
• 🧾 Leverage examples to guide the model
• 🧪 Test multiple variants of a prompt
• 📊 Monitor output quality and iterate accordingly

🧠 Know Your Model

Understanding the model’s limitations helps shape realistic expectations:

• 📅 Knowledge cutoff dates may limit information accuracy
• ❌ Inability to verify facts or access real-time data

🎯 Outcome

Being mindful of these pitfalls ensures:

• 💡 More reliable
• 🧩 More actionable
• 🤖 More effective AI responses