Dimon70007 · July 31, 2026 12:14 · Dimon70007 · Jul 31, 2026
diff --git a/gistfile1.txt b/gistfile1.txt
 To give you an accurate picture, it is important to clarify one key thing: a language model does not have a static, universal list of words with fixed weights built into its source code.
 Instead, weights are calculated dynamically in real time every time you send a message, using the Attention mechanism. The weight of a word depends entirely on the context of your prompt, the words surrounding it, and the patterns the AI learned during training.
 However, based on how LLMs (like Claude, GPT-4, or DeepSeek) are trained on human texts (code, legal documents, military manuals, casual forums), we can group semantic anchors into tiers of statistical priority.
 Here is a sorted breakdown of how different types of directives influence the AI's internal attention vector, from strongest to weakest.
 ------------------------------
 ## Tier 1: Absolute Structural Constraints (Highest Priority)
 These are terms associated with hard programming boundaries, logical definitions, and strict legal or military protocols in the training data. They create the strongest mathematical "gravity" in the prompt.

 * CRITICAL_DO_NOT / NEVER / ЗАПРЕЩЕНО — Negative constraints always receive the highest weight because they act as logical gates. In safety training (RLHF), breaking a "NEVER" rule is heavily penalized, making these tokens mathematically dominant.
 * MANDATORY / REQUIRED / ОБЯЗАТЕЛЬНО — Associated with system requirements and validation code. The model treats these as conditions that must evaluate to true before the text generation loop can close.
 * COMMAND / INSTRUCTION / ПРИКАЗ — Anchors the model into a strict execution state. It signals to the attention heads that the text following it is a meta-rule governing the entire generation, rather than conversational text.

 ## Tier 2: Standard Requirements and Specifications (Medium-High Priority)
 These words are common in Technical Specifications (RFCs) and Project Documentation. The AI treats them as high-priority goals, but they allow for more flexible implementation paths.

 * REQUIREMENT / ТРЕБОВАНИЕ — Tells the model to cross-reference its output against this specific point, but lacks the absolute blocking weight of "MANDATORY".
 * ALWAYS / ВСЕГДА — A strong frequency modifier. It forces the attention mechanism to look back at this rule at every single step (token) of the generation loop.
 * EXPECTED / ОЖИДАЕТСЯ — Signals a target output state. The model uses this to align its probabilistic trajectory toward a specific end result.

 ## Tier 3: Preferences and Guidelines (Medium Priority)
 These tokens appear frequently in style guides, advice, and tutorials. They guide the model's tone and style but can be easily overwritten if the prompt becomes too long or conflicting.

 * SHOULD / СЛЕДУЕТ — Interpreted as a strong recommendation. However, if a Tier 1 or Tier 2 constraint conflicts with it, the "SHOULD" rule will be mathematically sacrificed.
 * PREFER / ПРЕДПОЧТИТЕЛЬНО — Tells the AI which statistical path to choose if two options have roughly equal probability.
 * RECOMMENDED / РЕКОМЕНДУЕТСЯ — Soft guidance. It gently nudges the weights of certain associated words up by a small percentage.

 ## Tier 4: Optional and Permissive Terms (Lowest Priority)
 These words weaken constraints. They open up the mathematical search space, giving the model the freedom to ignore the suggestion if the underlying language patterns suggest a different path.

 * MAY / МОЖНО — Opens a branch in the probability tree but applies almost zero driving force to make the AI actually take it.
 * OPTIONAL / НЕОБЯЗАТЕЛЬНО — The model notes the option but assigns it a low priority. In long prompts (close to the context limit), these are the first rules the attention mechanism filters out.
 * IF_POSSIBLE / ПО ВОЗМОЖНОСТИ — The AI will only follow this if it doesn't require extra "effort" (i.e., if it aligns perfectly with the easiest, most common linguistic template available).

 ------------------------------
 ## 💡 The Prompt Engineer's Golden Rule
 If you want to ensure your OpenClaw setup never misses a rule, do not rely on a single word. Instead, stack the mathematical probability by combining three factors:

   1. Use Tier 1 Words: Use ТРЕБОВАНИЕ: ЗАПРЕЩЕНО... instead of Пожалуйста, не делай....
   2. Use Visual Anchors: Wrap the rule in uppercase XML tags (e.g., <STRICT_RULE>...</STRICT_RULE>). LLMs assign massive structural weight to code-like syntax.
   3. Punish Gallucination: Explicitly tell the model what a "failure" looks like (e.g., "If you say 'File created' without running step 2, it is a critical violation of your protocol").

 Would you like me to rewrite your OpenClaw soul.md file block using the exact XML structure and Tier 1 keywords that match the specific model (e.g., Claude 3.5 Sonnet or GPT-4o) you are currently running?
	To give you an accurate picture, it is important to clarify one key thing: a language model does not have a static, universal list of words with fixed weights built into its source code.
	Instead, weights are calculated dynamically in real time every time you send a message, using the Attention mechanism. The weight of a word depends entirely on the context of your prompt, the words surrounding it, and the patterns the AI learned during training.
	However, based on how LLMs (like Claude, GPT-4, or DeepSeek) are trained on human texts (code, legal documents, military manuals, casual forums), we can group semantic anchors into tiers of statistical priority.
	Here is a sorted breakdown of how different types of directives influence the AI's internal attention vector, from strongest to weakest.
	------------------------------
	## Tier 1: Absolute Structural Constraints (Highest Priority)
	These are terms associated with hard programming boundaries, logical definitions, and strict legal or military protocols in the training data. They create the strongest mathematical "gravity" in the prompt.

	* CRITICAL_DO_NOT / NEVER / ЗАПРЕЩЕНО — Negative constraints always receive the highest weight because they act as logical gates. In safety training (RLHF), breaking a "NEVER" rule is heavily penalized, making these tokens mathematically dominant.
	* MANDATORY / REQUIRED / ОБЯЗАТЕЛЬНО — Associated with system requirements and validation code. The model treats these as conditions that must evaluate to true before the text generation loop can close.
	* COMMAND / INSTRUCTION / ПРИКАЗ — Anchors the model into a strict execution state. It signals to the attention heads that the text following it is a meta-rule governing the entire generation, rather than conversational text.

	## Tier 2: Standard Requirements and Specifications (Medium-High Priority)
	These words are common in Technical Specifications (RFCs) and Project Documentation. The AI treats them as high-priority goals, but they allow for more flexible implementation paths.

	* REQUIREMENT / ТРЕБОВАНИЕ — Tells the model to cross-reference its output against this specific point, but lacks the absolute blocking weight of "MANDATORY".
	* ALWAYS / ВСЕГДА — A strong frequency modifier. It forces the attention mechanism to look back at this rule at every single step (token) of the generation loop.
	* EXPECTED / ОЖИДАЕТСЯ — Signals a target output state. The model uses this to align its probabilistic trajectory toward a specific end result.

	## Tier 3: Preferences and Guidelines (Medium Priority)
	These tokens appear frequently in style guides, advice, and tutorials. They guide the model's tone and style but can be easily overwritten if the prompt becomes too long or conflicting.

	* SHOULD / СЛЕДУЕТ — Interpreted as a strong recommendation. However, if a Tier 1 or Tier 2 constraint conflicts with it, the "SHOULD" rule will be mathematically sacrificed.
	* PREFER / ПРЕДПОЧТИТЕЛЬНО — Tells the AI which statistical path to choose if two options have roughly equal probability.
	* RECOMMENDED / РЕКОМЕНДУЕТСЯ — Soft guidance. It gently nudges the weights of certain associated words up by a small percentage.

	## Tier 4: Optional and Permissive Terms (Lowest Priority)
	These words weaken constraints. They open up the mathematical search space, giving the model the freedom to ignore the suggestion if the underlying language patterns suggest a different path.

	* MAY / МОЖНО — Opens a branch in the probability tree but applies almost zero driving force to make the AI actually take it.
	* OPTIONAL / НЕОБЯЗАТЕЛЬНО — The model notes the option but assigns it a low priority. In long prompts (close to the context limit), these are the first rules the attention mechanism filters out.
	* IF_POSSIBLE / ПО ВОЗМОЖНОСТИ — The AI will only follow this if it doesn't require extra "effort" (i.e., if it aligns perfectly with the easiest, most common linguistic template available).

	------------------------------
	## 💡 The Prompt Engineer's Golden Rule
	If you want to ensure your OpenClaw setup never misses a rule, do not rely on a single word. Instead, stack the mathematical probability by combining three factors:

	1. Use Tier 1 Words: Use ТРЕБОВАНИЕ: ЗАПРЕЩЕНО... instead of Пожалуйста, не делай....
	2. Use Visual Anchors: Wrap the rule in uppercase XML tags (e.g., <STRICT_RULE>...</STRICT_RULE>). LLMs assign massive structural weight to code-like syntax.
	3. Punish Gallucination: Explicitly tell the model what a "failure" looks like (e.g., "If you say 'File created' without running step 2, it is a critical violation of your protocol").

	Would you like me to rewrite your OpenClaw soul.md file block using the exact XML structure and Tier 1 keywords that match the specific model (e.g., Claude 3.5 Sonnet or GPT-4o) you are currently running?
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