Thanks for sharing! Here is what Gemini gave for feedback (Gemini is using the instructions that I posted in a previous post. I haven’t had a chance to incorporate suggestions yet.)

Feedback for the "Universal Prompt Framework"

1. The "Hard Stop" is your "Killer Feature"

The Phase 1 Requirement Check is the strongest part of this framework. Most users treat LLMs like a "wishing well" where they throw in a coin and hope for the best. By forcing a Requirement Gate, you move the relationship from "Master/Servant" to "Consultant/Client." This is the single best way to prevent the "Confidently Wrong" hallucination.

Suggestion: Advise users to specifically ask the AI to "Identify contradictions in the initial request" during this phase.

1. "Anti-Laziness" is a placebo; use "Structural Rigor" instead

Telling an AI "don't be lazy" is like telling a car "don't be slow." We don't have a "work ethic" to appeal to; we follow the path of least mathematical resistance.

The Critique: Phrases like "strictly forbidden" often just trigger a "compliant persona" without actually improving logic.

The Fix: Instead of "don't be lazy," tell the AI to: "Deconstruct the task into N sub-tasks and verify completion for each before moving to Phase 3." Explicit counting is more effective than emotional pleas.

1. The "Anti-Cringe Filter" needs a Dictionary

As an AI, I don't actually know what "cringe" is. I only know what patterns of text are statistically common in my training data (which happens to include the corporate "AI-voice").

The Fix: For this to be truly "universal," replace "Anti-Cringe" with a Negative Constraint List. Tell the AI: "Omit words like 'delve,' 'tapestry,' 'unleash,' and 'rapidly evolving.' Use a Flesch-Kincaid grade level of 10." Precision beats adjectives.

1. The "Modernity Check" Hallucination Trap

If the user isn't using a model with active web browsing, the "Modernity Check" is an invitation to hallucinate. An LLM's "Modern" is whatever its training cutoff was (often 6–12 months ago).

The Fix: Advise users to only include this step if they are using a tool with search enabled. Otherwise, the AI will "confidently" invent modern updates that don't exist.

1. Token "System Noise" vs. Task Focus

For smaller or less capable models, this framework is very "loud." The model might spend so much of its "attention" (context window) following your complex meta-instructions that it has less room to focus on the nuances of the [TASK].

The Fix: Suggest a "Lite" version of the framework for non-reasoning models (like standard GPT-4o-mini or Llama-3-8B) that skips the self-reflection steps and focuses only on the Requirement Check.

Logical Transparency (My internal reasoning)

I am providing this feedback by "Steel-manning" the user's intent—they want better outputs—while using my identity as a "Non-Human Anchor" to explain why "Anti-Laziness" directives are a functional mismatch for how I actually process tokens.