Most of us treat prompting like a casual conversation, but treating it more like code often yields significantly better results. If you feel like you’re constantly arguing with ChatGPT to get exactly what you want, you aren’t alone. This Reddit user finally sat down to read the entire OpenAI prompt engineering guide and realized their “skill issues” were actually just bad structural habits.
Quick Start
- What you’ll learn: Three specific structural shifts recommended by OpenAI to improve model compliance.
- What you need: Any LLM (ChatGPT, Claude, etc.) and your existing prompt library.
The Contrast: Conversation vs. Structure
The author discovered that many of us rely on “mega prompts” or conversational arguing to get results. However, the official documentation suggests that clarity comes from structure, not length. The user noted that since making these changes, they stopped having to fight the model to follow instructions. Interestingly, they also observed that the massive, complex prompts popular in 2024 seem to perform worse on newer reasoning models compared to these cleaner structures.
The 3 Rules You Were Missing
Here are the exact shifts the author implemented based on the official guide:
1. Delimiters are not optional
While it might look like a minor formatting choice, the guide places heavy emphasis on using clear separators.
- Why it matters: It prevents the model from getting lost in your source text. It ensures the AI knows exactly where the instructions end and the data begins.
- How to do it: Use specific characters like ### or “”” to separate your instructions from the context text.
2. Force “Hidden” Reasoning
For complex tasks, standard prompting often leads to errors. You need to explicitly force the model to show its work.
- Why it matters: The author found that forcing internal thought “kills about 80% of the hallucinations.”
- The Prompt: Include this exact instruction: “First, think through the problem step by step in a hidden block before giving me the answer.”
3. Positive Over Negative Constraints
Models are significantly better at processing “Do this” commands rather than “Don’t do that” restrictions.
- Why it matters: Negative constraints are harder for the model to parse and adhere to.
- How to do it: Instead of saying “don’t be wordy,” say “use a 3 sentence paragraph.” Always define what you want to see, rather than what you want to avoid.
Practical Next Steps
Review your current “go-to” prompts today. If you are using negative constraints (e.g., “no markdown”), rewrite them as positive instructions (e.g., “output as plain text”). Add delimiters to any prompt that involves pasting external text or data.
Check out the full discussion on Reddit for more community reactions.
Frequently Asked Questions
Q: Is there a similar guide for Anthropic (Claude)?
Absolutely! While OpenAI emphasizes delimiters like ###, Anthropic’s documentation leans heavily into using XML tags (like <context>) to structure prompts. The core principles of clarity remain the same, but checking their specific prompt engineering library is smart if you’re switching between models.
Q: Is it really better to avoid negative constraints?
It is a bit of a debate in the community, but the official docs are clear: positive instructions usually win. Telling the model "don’t do X" increases the cognitive load, whereas "do Y" gives it a clear path to follow. If you find the model ignoring your "don’ts," try flipping them into specific "do’s" (e.g., "keep it under 50 words" instead of "don’t be wordy").
Q: Are "Mega Prompts" still the best approach for new models?
Interestingly, many users are noticing that the massive, complex prompts of 2023 are performing worse on newer reasoning models. These new models often prefer concise, direct instructions rather than over-engineered structures. If your old super-prompts are failing, try stripping them back to the basics and letting the model’s internal reasoning take over.
I finally read through the entire OpenAI Prompt Guide. Here are the 3 Rules I was missing.
by u/Distinct_Track_5495 in ChatGPTPromptGenius