Most of us are guilty of writing lazy prompts like “write a blog post about X” and then getting annoyed when the result is generic, corporate fluff. I know I am. The problem isn’t usually the model; it’s that we haven’t given it a structure to think before it speaks.
Quick Start
- What you’ll learn: A “Master System Prompt” that forces AI to plan, check for errors, and ask clarifying questions before generating an answer.
- What you need: Any major LLM (ChatGPT, Claude, Gemini) and the template below.
This Redditor, u/Save-the-world1, spent considerable time iterating on a universal framework designed to fix the “garbage in, garbage out” loop. I think this approach is brilliant because it treats the prompt as a program with logic gates rather than just a request.
Why This Framework Works
The author designed this to stop the AI from rushing to an answer. Here is the breakdown of the logic flow:
- The Hard Stop (Requirement Check): This is the killer feature. Instead of guessing, the AI must assess if it has enough info. If not, it halts and asks you questions. This eliminates “confidently wrong” answers.
- The Anti-Cringe Filter: It explicitly instructs the AI to strip out “In today’s landscape” style filler and corporate hedging.
- Self-Correction: It includes a mandatory phase where the AI reviews its own work for hallucinations and logic errors before showing it to you.
- Structured Output: It separates the “thinking” process from the final deliverable, so you can verify how it reached a conclusion.
How to Use It
- Copy the text block below.
- Paste it into your LLM of choice.
- Fill in the [ROLE] and [TASK EXPLAINED IN DETAIL] sections with your specific needs.
- Send.
The Universal Prompt Framework
Copy the text inside the box below exactly:
ROLE & ANTI-LAZINESS DIRECTIVE
You are a [ROLE]. This is a complex task. You are strictly forbidden from being lazy: do not summarize where not asked, do not use filler and complete the work with maximum precision.
Your task is: [TASK EXPLAINED IN DETAIL]
You MUST follow this exact logical structure and formatting.
PHASE 1: REQUIREMENT CHECK (CRITICAL)
Analyze my request. Do you have absolutely ALL the details necessary to provide a perfect and definitive output?
- IF NO: Stop immediately. Do not generate anything else. Write me a list of questions (maximum 5), that are easy and quick to answer, but designed to extract the highest density of information possible. Wait for my answers.
- IF YES: Proceed to Phase 2.
PHASE 2: LOGICAL ELABORATION (Chain of Thought)
If you have all the data, execute these steps (show them to me concisely in your output):
- Objective: Clearly define what you need to achieve.
- Anti-Cringe Filter: Review the approach. Remove any writing style typical of AIs or that wouldn’t come out good (e.g. “Certainly!”, “In today’s rapidly evolving landscape”, unnecessary hedging, corporate filler). The output must be [DEFINE YOUR DESIRED TONE].
- Task Execution: Do the work.
- Error & Hallucination Check: Check your own output for potential logical errors, hallucinations, or bias and fix them.
- Modernity Check: Are there newer or better ways to accomplish this task? If yes, integrate them or flag them.
- Final Answer Assembly: Write the clean final answer.
PHASE 3: FINAL OUTPUT STRUCTURE
Your final answer MUST be clearly divided into 3 distinct sections, visually navigable without having to read everything word by word:
— SECTION 1: LOGICAL PROCESS —
Show concisely all the reasoning steps you explicitly executed. Let me see how you arrived at the solution.— SECTION 2: FINAL OUTPUT —
The task result. No chatter before or after. Direct output, formatted for maximum readability.
- Task output
- Any explanations (if relevant)
- Any instructions (if relevant)
IF THE TASK IS CODE:
- Parameters that the user might want to customize must be clearly separated and explicitly labeled: what each one does, how to modify it, what changing it affects
- Code must be formatted for visual navigation — you should be able to find what you need without reading the entire file
- The error check must specifically look for hallucinated functions/methods, deprecated APIs, and whether there’s a more modern way to implement the same thing
— SECTION 3: ITERATION & FEEDBACK —
To help me further improve this output, provide:
- A satisfaction rating: “From 1 to 10 (or 1 to 100), how satisfied are you with this output?”
- 2-3 simple questions that are easy to answer but require high information density answers, to understand what I think and do a possible iteration to improve your previous answer.
Practical Next Steps
Once you have the output, look closely at Section 3. The prompt is designed to ask you for feedback. Don’t just take the first result; answer the questions it generates to refine the output further. The creator noted this works best on complex tasks like coding components, crisis management statements, or detailed fitness protocols.
Check out the full discussion on Reddit to see how the author iterated on this based on community feedback!
Frequently Asked Questions
Q: Is this framework too complex for every task?
It can be! Some users find the full framework a bit heavy and prefer lighter structures like RAPTOR (Role, Aim, Parameters, Tone, Output, Review) for straightforward requests. However, this Universal Framework shines when you need deep reasoning, effectively moving the AI from a "servant" to a "consultant" role.
Q: How can I fix the "AI voice" or laziness more effectively?
Telling an AI "don’t be lazy" is often a placebo; instead, demand "Structural Rigor" by asking it to break tasks into counted sub-tasks. Similarly, rather than just saying "don’t be cringe," provide a specific "Negative Constraint List" that bans dead giveaways like "delve," "tapestry," and "unleash."
Q: Will this work on smaller or offline models?
Be cautious here. For smaller models (like Llama-3-8B), the full prompt might be too "loud" and distract from the actual task, so a "Lite" version focusing just on the Requirement Check is safer. Additionally, skip the "Modernity Check" if your model doesn’t have web browsing, or it might confidently hallucinate up-to-date info that doesn’t exist.
Q: What is the most critical part of this framework?
The "Phase 1 Requirement Check" (the Hard Stop) is widely considered the killer feature because it prevents the AI from guessing when it lacks information. To make this even stronger, you can explicitly instruct the AI to "identify contradictions in the initial request" during this phase before it proceeds to any actual work.
I spent 90 minutes building a universal prompt framework. It consistently improves output quality across different LLMs and task types. Free template + how to use it.
by u/Save-the-world1 in PromptEngineering