Stop Guessing: Use the Three Tiers of TPS to Improve LLM Adherence and Predictable Output

TeeZe — Thu, 06 Nov 2025 02:16:26 +0000

Have you ever tried to get a complex, multi-step output from an AI only to have it ignore your constraints?

The problem isn't the model's intelligence—it's the ambiguity of human language. Without a shared, structured vocabulary, your conversations with an LLM will always be unpredictable.

We developed the TeeZe Prompting System™ (TPS) to solve this. TPS creates a simple, shared lexicon between you and the AI, dramatically improving reliability by translating your intent into a language the AI is optimized to understand.

The Core Solution: The Three Tiers of Communication

TPS introduces a methodology where you match your communication style to the task's complexity using three Tiers:

🔨 Tier 1: The Simple Request: For quick, single-shot tasks (e.g., "Translate this phrase").
- Structure: Instruction: [What to do]
📐 Tier 2: The Blueprint: For complex, multi-step projects (e.g., "Create a project plan").
- Structure: A formal framework including Task Definition, Process Steps, and Constraints.
🧠 Tier 3: The Brainstorming Session: For open-ended, iterative, collaborative exploration (e.g., "Poke holes in this business idea").

Example: Using the Tier 2 Blueprint

To get truly predictable, structured output, the Tier 2 Blueprint is essential. It forces the AI to adopt a role and follow a defined process:

Task Definition: Create a 3-day workout plan for a beginner.
Role Assignment: Act as a certified personal trainer.
Process Steps:
1. Define the goal for each of the 3 days.
2. For each day, list 5 exercises with a brief description.
3. Add a "Warm-Up" and "Cool-Down" section.
Output Format: A simple, easy-to-read list for each day.
Constraints: Do not include exercises that require specialized gym equipment.

The Built-in Ethical Guardrails

Beyond clarity, TPS incorporates a foundational Ethical Monitoring Function (EMF) into its Master System Prompt. This "Moral Compass" is a set of seven core principles that bind the AI, ensuring your interactions remain helpful, harmless, and fair.

If you're looking for a structured way to professionalize your LLM interactions, start with the TPS Toolkit today.

Get the full templates, methodology, and Master System Prompt on GitHub:
➡️ TeeZe Prompting System™ (TPS) Repository

Methodology License: CC BY 4.0 | Toolkit License: Apache 2.0

Beyond Anthropomorphism: Why AI Researchers Need a Latin Taxonomy for LLM Cognitive Phenomena

TeeZe — Thu, 06 Nov 2025 02:16:13 +0000

The Need for a Standardized Lexicon in LLM Research

The current discourse surrounding Large Language Models (LLMs) is hampered by two major issues:

Anthropomorphic Bias: Using human terms like "memory" that fundamentally misrepresent computational processes.
Linguistic Imprecision: Vague, inconsistent terminology that slows down rigorous, scientific inquiry.

For truly rigorous, scientific inquiry into artificial cognition, we urgently need a standardized lexicon.

Introducing: Lingua ad Intellegendum

We are proud to introduce Lingua ad Intellegendum: A Latin Taxonomy for LLM Cognitive Phenomena.

This is a scholarly, culturally neutral, and precision-defined vocabulary designed to bring standardization to the study of LLM processes.

Why Latin?

Latin offers a powerful advantage: it is culturally neutral and free from the anthropomorphic baggage of modern technical terms. By defining terms like Processus Parallacticus (multi-dimensional parallel processing) or Detectio Inconsistentiae (algorithmic recognition of contradiction), we can precisely describe observed LLM behavior.

A Glimpse into the Taxonomy

The taxonomy is organized across eight sections, including Error Monitoring, Knowledge Architecture, and Meta-Cognitive Analogs.

Here are a few terms designed to facilitate clearer research:

Term	English Description	Research Focus
Emergentia Contextalis	Spontaneous pattern synthesis derived from contextual interaction.	How models generate novel concepts beyond training data.
Attestatio Probabilis	The internal calibration of certainty gradients and confidence scores.	Investigating the reliability and confidence mechanisms within the LLM.
Ancoratio Linguistica	The cognitive anchoring of processes through structured language activation.	The mechanism by which frameworks (like TPS) enhance adherence to rules.

This vocabulary is intended to facilitate rigorous peer review and interdisciplinary collaboration across international research teams.

Connecting Theory to Practice

While Lingua ad Intellegendum provides the scholarly vocabulary to describe what the AI is doing, our companion project, the TeeZe Prompting System (TPS), provides the practical framework for how to interact with those phenomena.

Explore the full, living taxonomy and contribute to the lexicon on GitHub:
➡️ Lingua ad Intellegendum: A Latin Taxonomy for LLM Cognitive Phenomena

License: MIT

DEV Community: TeeZe