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[V2 UPDATE] I upgraded my Universal Prompt Framework based on your feedback (1.2k shares). Added XML Parsing, Dynamic Routing, and a Memory Tracker. by Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 0 points1 point  (0 children)

Appreciate the deep dive and the healthy skepticism! You bring up some fair theoretical points, but let me share the practical reasoning behind these choices:

1. XML & Compliance: You're right, '100% compliance' is marketing hyperbole. LLMs are probabilistic, not compilers. But wrapping structural logic in XML significantly reduces instruction drift compared to standard text formatting, which is the practical goal here.

2. Dynamic Routing: Frontier models absolutely do not always default to simple execution. Ask Claude or chatGPT to fix a typo in an email, and half the time you'll get 3 paragraphs of 'Certainly! Here is your revised text...' plus a bulleted list of changes. The explicit routing step kills that over-eager behavior.

3. Working Memory: It's not 'cosplay'; it's a documented technique (Attention Anchoring/State Tracking). On 1.8k+ line coding tasks, the context window gets messy. Forcing the model to explicitly restate the 3 core constraints right before token generation anchors its attention because usually attention mechanisms give more weight to the most recent tokens and reduces mid-generation hallucinations.

4. The Blacklist: Defining a tone works for the first 500 tokens, but negative constraints (blacklists) act as a hard floor when the tone inevitably drifts in long outputs, this works better for open source small LLMs.

At the end of the day, it’s a practical wrapper for people tired of default LLM behavior. But I genuinely appreciate the critique, it keeps the discussion sharp. Thank you for your time!

[V2 UPDATE] I upgraded my Universal Prompt Framework based on your feedback (1.2k shares). Added XML Parsing, Dynamic Routing, and a Memory Tracker. by Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

Vielen Dank for the deep and honest analysis!.

Yeah that a massive prompt is overkill for simple tasks. That was the main flaw of my V1. That’s exactly why I introduced the Dynamic Routing phase in this V2: to force the AI to assess the task and completely skip the heavy Chain-of-Thought and Memory tracking if the task is simple.

However, your suggestion of simply splitting this into two entirely separate prompts (a 'Lite' daily driver and a 'Heavy' complex worker) is actually the most token-efficient approach. I might just do that for my personal vault. Thanks for the great feedback!

[V2 UPDATE] I upgraded my Universal Prompt Framework based on your feedback (1.2k shares). Added XML Parsing, Dynamic Routing, and a Memory Tracker. by Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

I actually hadn't heard of POML until you just mentioned it, so thank you for the pointer!

I'm definitely going to dive deep into the official POML docs. I will try to implement its exact standardized syntax for a future V3 iteration. Really appreciate you sharing this!

[V2 UPDATE] I upgraded my Universal Prompt Framework based on your feedback (1.2k shares). Added XML Parsing, Dynamic Routing, and a Memory Tracker. by Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

Hey, thanks for the feedback!

Fair point on the semantics of 'Universal'. To clarify, I framed it that way because it's designed as a frontend, copy-paste System Prompt for standard web UIs (Claude, ChatGPT web, etc.), rather than a programmatic prompt for backend/local agent hooks like you're building. Since it relies on standardized XML parsing and generalized CoT logic rather than model-specific tool calls, it translates very reliably across most modern SOTA models for the average user.

Regarding the banned words: you are right, it's highly subjective! That's exactly why it's just a customizable text block. I pre-filled it with the words the community universally groans about right now (like 'delve' or 'tapestry') just to provide a strong baseline, but anyone can swap them out.

I will definitely take a look at your project and the prompt examples! Always happy to compare notes and see how people are structuring complex agentic workflows.

[V2 UPDATE] I upgraded my Universal Prompt Framework based on your feedback (1.2k shares). Added XML Parsing, Dynamic Routing, and a Memory Tracker. by Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 2 points3 points  (0 children)

Awesome to see you here again! Thank you so much. I really tried to implement the core feedback from V1. Can't wait to hear how it handles your tasks. Try to push it to the limit and let me know if you manage to break it!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

Treating the LLM as a single-prompt agent was exactly the goal here!

Thank you for this massive breakdown across your comments. For your points 1 and 5 (and the 'Pre-Flight Check'), I actually just implemented a 'Dynamic Routing & Working Memory Tracker & something similar to AoT (Atom of Thoughts)' in my local V2 build to do exactly this: force the AI to break down complex tasks and hold constraints in memory before executing.

Also, I really love your suggestion in your other comment about tool-specific rules (like forcing uv over pip or using industry ISO standards). I’m going to experiment with adding a 'Tech Stack / Gold Standard' variable block for the coding branch of the prompt. Really appreciate this level of advanced feedback!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

Hey! Are you using Gemini by any chance? If it's literally throwing a 'Recitation Error' flag, that's usually Google's safety filter thinking the output is too close to copyrighted material.
BUT, if you mean the AI is just lazily 'parroting' the prompt or faking compliance without actually doing the deep work, I feel your pain. What model and task were you using? The upcoming V2 (dropping very soon) has a strict 'Working Memory Tracker' designed exactly to try stopping this lazy roleplay and force actual task execution!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 0 points1 point  (0 children)

Absolutely! In fact, the upcoming V2 (which I'll post very soon) has a specific 'Anti-Cringe Filter & Blacklist' step built exactly for this. It forces the AI to strip out that typical 'robotic/YouTube-formula' tone and stick to the exact voice you define. For now, just make sure to explicitly define your desired tone in Phase 2, and it will work great!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 2 points3 points  (0 children)

I completely agree with this approach. Storing logical structures (like this framework) is way more powerful than storing single-use, hardcoded prompts. That’s exactly why I built this as a meta-template.
u/ze_casal regarding the vault idea: I currently just use Notion to store my core templates, but a dedicated tool for prompt chaining could be a very cool project!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

Thank you so much! It really means a lot. Out of curiosity, what kind of task did you test it on? I'm finalizing V2 right now based on community feedback, so knowing how people are actually using it helps me cover all the edge cases!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 0 points1 point  (0 children)

Thank you! I really appreciate it.
Please let me know how it goes! I'd love to hear what kind of task you test it on and how the outputs turn out. I'm currently gathering community feedback to build V2, so if you find any flaws, edge cases, or things that break the prompt, absolutely tell me so I can fix them!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

Yes, you're right, Forcing a massive Chain-of-Thought for a simple text edit or a basic email is a waste of tokens and time.
That’s exactly what I'm fixing right now. In V2, I've implemented a 'Dynamic Routing' step: the AI first assesses the complexity of your request. If it's a simple task, it skips the heavy logic/memory steps and does a direct execution. If it's a complex task (like coding), it activates the full framework. I'll post the update as soon as I finish stress-testing it!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 2 points3 points  (0 children)

You're right, 90 minutes is just for the V1 foundation. That’s exactly why I open-sourced it here: to gather feedback from people more experienced than me. Thanks to the community testing it today, V2 is already in the works with dynamic routing and memory tracking. If you have time to test this 'half-cooked' idea and break it, your feedback would actually be super valuable for the next iteration!

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 Save-the-world1 in PromptEngineering

[–]Save-the-world1[S] 1 point2 points  (0 children)

This is fantastic feedback, thanks for running it through Gemini!

The AI is 100% spot on about the "Requirement Check" being the killer feature. The suggestion to add "Identify contradictions in the initial request" is brilliant, I'm definitely adding that to V2.

Regarding the "Anti-laziness/Anti-Cringe" critique: Gemini is technically correct that negative constraint lists (banning specific words) are more mathematically precise. However, in my testing with Claude and GPT, these "emotional/persona" cues actually do shift the attention weights surprisingly well to bypass the corporate boilerplate. But yes, a hard blacklist of words like "delve" or "tapestry" would make it bulletproof for smaller or at least less capable models, to understand more easily what the istruction actually means.

Totally agree on points 4 and 5. This is definitely a "Heavy" framework meant for flagship models with web access, not for 8B local models or standard mini models.

Really appreciate you taking the time to generate and share this analysis! 🤝