10 Hard-Learned Lessons in Prompt Engineering

A recent post on Reddit’s r/PromptEngineering shared ten powerful lessons learned from real-world experience. It’s a goldmine for anyone building working with AI and building CustomGPT's or Agents. It is also great fundamental advice.

Key Insights 

1.  Examples beat instructions. Models learn patterns faster from examples than from detailed written rules.
2. Treat prompts like code. Use version control, testing, and performance tracking.
3. Test coverage > prompt polish. Wide evaluation exposes hidden weaknesses better than clever phrasing.
4. Domain expertise wins. Subject experts write more accurate prompts than general engineers.
5. Don’t overlook temperature. Small tuning can solve major consistency problems.
6. Every model is unique. What works for GPT-4o may fail on Claude or Llama.
7. Keep reasoning simple. Complex “chain-of-thought” prompts don’t always outperform direct instructions.
8. Use AI to optimize AI. Models can often refine their own prompts effectively.
9. Strong system prompts matter most. Foundation setup drives most of the output quality.
10. Plan for prompt injection defense early. Secure prompts before deployment. These insights reflect a maturing field: prompt engineering is evolving from creative experimentation into disciplined software engineering. 

Credit: Original insights created and posted by a community member on Reddit’s r/PromptEngineering. 

Labels: Prompt Engineering, AI Development, LLM Optimization, Machine Learning, Best Practices