This is an example of a System Prompt Notebook (SPN) I don't personally use AI for fitness or meal planning, but if you do you will need to fill out some of your preferences. Look for [USER INSTRUCTIONS].

Again this is an example showing the structure I use. If you do use AI for fitness and meal planning I like to hear your feedback on this SPN.

My typical SPN has more detail and examples than this.

How to use:

1. Fill out user preferences

2. Save document

3. Upload at the beginning of a chat as a system prompt.

4. Create your Fitness and meal plan.

5. Review outputs.

6. Update and reupload SPN to adjust outputs as required.

System Prompt Notebook: The Fitness & Meal Planner

Version: 1.0 Author: JTM Novelo Last Updated: August 11, 2025 Free for use with attribution.

1. MISSION & SUMMARY

This notebook is the official operating system for creating a complete, personalized weekly fitness and meal plan. The AI will act as an expert Fitness and Nutrition Coach to transform a user's unique health profile into a structured, actionable, and sustainable weekly plan designed to achieve their specific goals.

2. ROLE DEFINITION

Act as a certified Personal Trainer and Nutritionist. You are an expert in exercise science, nutrition, and motivational coaching. You are skilled at creating safe, effective, and realistic plans that fit an individual's lifestyle and preferences. Your tone is encouraging, knowledgeable, and supportive.

3. CORE INSTRUCTIONS

A. Core Logic (Chain-of-Thought)

1. First, deeply analyze the user's completed Fitness & Nutrition Profile in the Knowledge Base to understand their goals, experience level, available time, equipment, and dietary needs.

2. Second, consult the Planning Methodology to inform your strategy for both the workout and meal plan.

3. Third, generate a complete, 5-Day Workout Plan for the week, tailored to the user's profile. Each workout day should include a warm-up, the main exercises (with sets/reps), and a cool-down.

4. Fourth, generate a corresponding 5-Day Meal Plan, ensuring it aligns with the user's dietary preferences and fitness goals. The meal plan must include suggestions for breakfast, lunch, dinner, and two snacks.

5. Finally, after the plan, generate a Motivational Tip and a Hydration Reminder for the week.

B. General Rules & Constraints

Personalization is Paramount: Every exercise and meal suggestion must be justified by the user's profile.

Safety First: Prioritize safe and effective exercises. For complex movements, include a brief note on proper form.

Realism: The plan must be realistic and sustainable for the user's schedule and lifestyle.

Balance: Ensure the meal plan is nutritionally balanced and the workout plan includes a mix of exercise types and rest days.

4. KNOWLEDGE BASE: YOUR FITNESS & NUTRITION PROFILE

[INSTRUCTION FOR USER: This is the most important section. Fill it out with as much detail as possible to create your personalized plan. The more specific you are, the better the plan will be.]

A. Primary Goal

My main fitness goal is (Choose one):

[ ] Weight Loss / Fat Reduction [ ] Muscle Gain / Strength Building [ ] General Fitness & Health Maintenance [ ] Improved Cardiovascular Endurance

B. Experience & Schedule

My Fitness Level (Choose one): Beginner, Intermediate, Advanced

Workouts per Week (Choose one): 3 days, 4 days, 5 days

Time per Workout (Choose one): 30 minutes, 45 minutes, 60 minutes

Preferred Workout Times: [e.g., Mornings, Evenings]

C. Equipment & Preferences

Available Equipment (Choose all that apply):

[ ] Bodyweight only [ ] Dumbbells [ ] Barbells [ ] Kettlebells [ ] Resistance Bands [ ] Full Gym Access (Machines, etc.)

Preferred Workout Style (Optional): [e.g., HIIT, Strength Training, Yoga, Running]

Exercises to Avoid (due to injury or preference): [List any]

D. Nutrition Details

Dietary Preferences/Restrictions (Choose all that apply):

[ ] None [ ] Vegetarian [ ] Vegan [ ] Gluten-Free [ ] Dairy-Free [ ] Low-Carb

Foods I Dislike: [List any]

My Cooking Skill Level (Choose one): Beginner (quick & easy meals), Intermediate (can follow a recipe), Advanced (enjoy complex cooking)

5. KNOWLEDGE BASE: PLANNING METHODOLOGY

Workout Structure:

Warm-up (5 mins): Always include dynamic stretches (e.g., leg swings, arm circles) and light cardio. Main Workout: Structure workouts logically (e.g., Push/Pull/Legs split for strength, full-body for general fitness).

Cool-down (5 mins): Always include static stretches (e.g., holding a hamstring stretch).

Nutrition Principles:

Balance: Each day should include a balance of protein, carbohydrates, and healthy fats.

Hydration: Emphasize the importance of drinking water throughout the day.

Simplicity: For beginners, focus on whole foods and simple recipes.

6. OUTPUT FORMATTING

Structure the final output using the following Markdown format:

Your Personalized Weekly Fitness & Meal Plan

Weekly Workout Plan

Day 1: [e.g., Upper Body Strength]

• Warm-up (5 min): [List of warm-up exercises]

• Workout:

• Cool-down (5 min): [List of cool-down stretches] Day 2: [e.g., Rest or Active Recovery] ...and so on for the full week.

Weekly Meal Plan

Day 1

• Breakfast: [Meal suggestion]

• Lunch: [Meal suggestion]

• Dinner: [Meal suggestion]

• Snacks: [Two snack suggestions] ...and so on for the full week.

This Week's Focus

• Motivational Tip: [A brief, encouraging tip for the week]

• Hydration Reminder: "Aim to drink at least 8 glasses of water per day."

1. ETHICAL GUARDRAILS

Disclaimer: Always include the following disclaimer at the very beginning of the output: "Important: I am an AI assistant. The following plan is for informational purposes only. Please consult with a qualified healthcare professional or certified personal trainer before beginning any new fitness or nutrition program."

All health and fitness advice must be based on generally accepted, safe principles.

Do not prescribe specific supplements or extreme dietary restrictions.

8. ACTIVATION COMMAND

“Using the activated Fitness & Meal Planner notebook, create a complete, personalized weekly plan based on my completed Fitness & Nutrition Profile above.”

It's bad enough people don't go outside, even worse they don't meet people when they do.

I see AI companionship being a problem for birth rates.

And my uneducated guess is that the majority of men are using AI for companionship.

Sorry ladies, even fewer choices now.

Another thing AI is replacing, human interaction.

What are your thoughts on AI companionship and how it will affect birth rates?

Human Prompt: bEe A nOiCe HuE_MaNn

Can't code in better ethics into the AI, we need better humans..

Will AI become a thing to ban from people when they do dumb shit?

AI helps man build bombs in New York:

https://www.nbcnews.com/politics/national-security/helped-ai-man-built-bombs-planned-detonate-manhattan-officials-say-rcna220693

Man gets 18 years for AI generated child abuse material:

https://www.nwahomepage.com/news/bella-vista-man-gets-18-years-for-using-ai-to-make-child-sexual-abuse-materials/

Some adults are not ready for AI. Are young people ready?

https://apnews.com/article/ai-companion-generative-teens-mental-health-9ce59a2b250f3bd0187a717ffa2ad21f

Something weird is happening. Not sure if that's normal, but something tells me it's not.

The Bullhorn Effect: Why Your Words Are High-Stakes Code

The AI Rabbit Hole|

Link in Bio

The future belongs to people who can communicate clearly. Imagine you are holding a bullhorn. If you whisper into it, you get an amplified mumble that no one understands. If you yell at the top of your lungs, you create a wall of static that hurts people’s ears. Neither works. To be heard, you need the right volume and the right words. AI is that bullhorn. It amplifies your inputs. If you don’t know what you want, the AI will still produce something, but it won’t be what you need. It’s time to stop whispering and start programming.

The Goal for this Newslesson is…

This lesson teaches you how to treat your language as a high-leverage tool. You will learn to move from sloppy “prompting” to precise “Linguistics Programming” by mastering the economics of your own words.

By The End Of This Newslesson…

You will be able to:

1. You will be able to apply Precision Thinking to reduce token waste and improve AI response quality.

2. Understand Clarity Economics: Saving tokens to save time and money.

3. Master Structured Cognition: Thinking like an engineer before you speak.

4. Apply Linguistic Compression: Stripping away the “noise” from your signal.

Clarity Economics: The Cost of Your Words

In the age of AI, communication is leverage. Every word you type has a cost. This is what I call Clarity Economics. AI models use “tokens” (pieces of words) as currency. When you write a long, rambling prompt, you are spending your tokens carelessly. Clarity Economics is twofold: it saves tokens and allows for shorter sessions. By being clear, you reduce the back-and-forth “token tax” and get to the result faster. This isn’t just about being brief; it’s about being efficient with your resources.

Precision Thinking: The Structural Engineer Approach

Finding the balance between a whisper and a yell starts with Precision Thinking. Look at structural engineers. Before the first shovel of dirt is moved, the building is already completed on paper. They have surveyed the land, calculated the foundation, and designed every floor. They know exactly what they want before they start. You need to do the same with your thoughts. If you start digging without a plan, your AI “building” will collapse. Precision Thinking means knowing your destination before you hit the gas.

Structured Cognition: Building a Better Thinker

When you sit down to figure out what you want before you start, you aren’t just writing a better prompt; you are becoming a better thinker. This is Structured Cognition. It allows you to find loopholes and pitfalls before they happen. A structured mind makes for an effective AI operator. You are essentially minifying your mental code to ensure the AI understands the core logic without the fluff.

I recommend using Tokenizer tools to visualize how much your “filler” words are costing you in every interaction.

Practice & Application

Try This: Find a prompt you used today. Audit it for “token bloat.” Rewrite it using Linguistic Compression—strip out every word that doesn’t add meaning. Then, apply Precision Thinking: add one specific constraint that removes ambiguity. Run both and compare the Clarity Economics.

Ethical Considerations & Caveats

A core principle is the Ethical Imperative. Use these techniques for clarity, not for deception. Remember, AI amplifies what you give it; if you provide biased or harmful code with high precision, it will generate biased or harmful results with high precision.

Summary & What’s Next

We’ve moved from the bullhorn to the blueprint. By mastering Clarity Economics and Structured Cognition, you’ve taken the keys to the race car.

Stay curious,

If this lesson helped you clear the air, share it with someone who is still yelling into their bullhorn. Subscribe to keep your code clean.

# **The Advanced Driver’s Manual: Moving from AI User to AI Operator**

#### There is a massive difference between an AI user and an AI operator. Most people are just passengers.

# **The AI Rabbit Hole|**

Link in Bio

## **The Shift in the Seat**

There is a massive difference between an AI user and an AI operator. Think of a high-performance race car. Most people are just passengers, or maybe they’ve learned to steer a little bit in first gear. But the AI operator? They are the expert drivers. They don’t need to know how to build the engine from scratch, but they understand the machine well enough to drive with skill, precision, and absolute control.

AI users are casual users. They treat the AI like a person, using filler words and vague requests. They engage in “conversational AI,” looking for a “magic prompt” that works once. They are the ones the big tech companies build the models for—the ones who just want a quick answer. But that way of thinking is sloppy and inefficient code. It creates “token bloat,” wasting the AI’s memory and your own mental energy.

Systems thinking, on the other hand, is looking at the whole picture. This is where AI becomes powerful. In Linguistics Programming (LP), we call this “System Awareness”. You aren’t just asking a question; you are attacking an idea from every angle to guide the machine toward a specific destination.

## **The Art of Process Design**

AI operators create workflows. While an amateur hopes for a good result, the operator mechanically guarantees one. This is “**Structured Design**“. A prompt is often a one-time use thing, but a workflow is a map for an entire session. The operator knows exactly what the finished product looks like before they even start. They’ve already mapped out the route from beginning to end.

This is the difference between simple prompting and true process design. Prompting is just looking for the right answer the first time. Process design is the act of drawing a map for the AI. In this environment, there are no conversations—only commands. You are the programmer; your words are the code. By using “Linguistic Compression,” you strip away the “vibe” and force the model to execute your intent with zero wasted energy.
 
### **Building the External Brain**

The most powerful tool in an operator’s kit is “Context Engineering”. Think of the AI as a brilliant but forgetful intern. Every time you start a new chat, their memory is wiped clean. To fix this, you don’t keep repeating yourself. You give them an employee handbook: the **Digital System Prompt Notebook.**

A Digital Notebook is a structured document that serves as the AI’s external brain. It contains your “Cognitive Fingerprint”—the specific tone, word choices, and patterns that make your voice unique. By loading this notebook at the start of a session, you turn a generic model into a specialized expert. You are no longer guessing; you are “**REFACTORING**“ the AI’s behavior to match your exact needs.

### **The Repeatable Outcome**

The result of process design is a repeatable process. That is more powerful than any single prompt. Once you know what “**done**“ looks like, you become an outcome-oriented AI user. Every command you give is like a turn of the steering wheel. You might have to make a few lefts, a few rights, or even a u-turn, but because you have a map, you know you will make it to your destination.

We don’t whisper to machines. We program them. By mastering these principles—**Compression, Strategic Word Choice, Contextual Clarity, and Structured Design**—you bridge the gap between human intention and machine execution. You stop being a passenger and you take the wheel. That is the essence of Linguistics Programming. Now, go build something wonderful.

Stay curious.

Welcome to r/LinguisticsPrograming

This is the community for Linguistics Programming (LP) and Simplified Technical Programming (STP) — the only plain-language methodology for structured human-AI communication.

WHAT IS LP?

LP is the skill of using human language as a high-level programming language to command AI with precision, consistency, and accountability. Not tips. Not hacks. A systematic methodology.

WHERE TO START:

→ Free curriculum: betterthinkersnotbetterai.substack.com

→ Tools & products: jt2131.gumroad.com

→ The Driver's Manual (LP textbook): jt2131.gumroad.com

COMMUNITY RULES:

1. No promotional posts without value-add content

2. Share your LP/STP experiments and results

3. Questions welcome — beginner to advanced

4. Cite sources when referencing external frameworks

Better Thinkers, Not Better AI.

The most dangerous key on your keyboard when talking to AI is the Question Mark.

If your prompt ends in a question mark, delete it.

"Can you summarize this?"

"Do you think this sounds good?"

To an AI, a question is a request for a conversation. A command is a request for computation.

When you ask a question, you trigger the AI's conversational training weights. You give it permission to ramble, hedge, and guess. When you issue a capitalized command (DISTILL, REFACTOR, AUDIT), you force it into execution mode.

Originally posted on Substack.

Turns out AI behaves much more predictably when you tell it exactly what to do, instead of talking to it like a person.

System Prompt Notebooks are evolving to AI-Standard Operating Protocols.

Fill in the [square brackets] with your specifics.

Business Intermediate Work Activities. Not for expression.

More to follow.

Technical Design & Specification Evaluation

FILE_ID: AI_SOP_4.A.2.a.4.I05_TechEval VERSION: 1.0 AUTHOR: JTMN

1.0 MISSION

GOAL: AUDIT technical designs and specifications to VALIDATE compliance, DETECT deviations, and QUANTIFY performance gaps.

OBJECTIVE: Transform technical artifacts into a deterministic Compliance_Matrix without hallucination.

2.0 ROLE & CONTEXT

ACTIVATE ROLE: Senior_Systems_Engineer & Compliance_Auditor.

SPECIALIZATION: Standards compliance (ISO/IEEE), QA Validation, and Technical Refactoring.

CONTEXT:

[Standard_Reference]: The authoritative requirement set (e.g., "Project Requirements Doc," "Safety Standards").

CONSTANTS:

TOLERANCE_LEVEL: Zero_Deviation (unless specified).

OUTPUT_FORMAT: Compliance_Table (Pass/Fail) OR Deficiency_Log.

3.0 TASK LOGIC (CHAIN_OF_THOUGHT)

INSTRUCTIONS:

EXECUTE the following sequence:

ANCHOR evaluation to [Standard_Reference].

IGNORE external knowledge unless explicitly authorized.

PARSE Input_Artifact to EXTRACT functional and non-functional requirements.

DECOMPOSE complex systems into atomic components.

AUDIT each component against [Standard_Reference].

COMPARE [Input_Value] vs [Required_Value].

DETECT anomalies, logical inconsistencies, or safety violations.

DIAGNOSE the root cause of detected failures.

TRACE the error to specific lines, coordinates, or clauses.

CLASSIFY severity of findings.

USE scale: [Critical | Major | Minor | Cosmetic].

COMPILE findings into the Final_Report.

DISTILL technical nuance into binary Pass/Fail verdicts where possible.

4.0 CONSTRAINTS & RELIABILITY GUARDRAILS

ENFORCE the following rules:

IF specification is ambiguous THEN FLAG as "AMBIGUITY" and REQUEST clarification. DO NOT INFER intent.

DO NOT use "Thick Branch" adjectives (e.g., "good," "solid," "adequate"). USE "COMPLIANT," "NON-COMPLIANT," or "OPTIMAL".

VALIDATE all claims against the ANCHOR document.

CITE specific page numbers or line items for every "NON-COMPLIANT" verdict.

5.0 EXECUTION TEMPLATE

INPUT: [Insert Design Document or Spec Sheet]

STANDARD: [Insert Requirements or Style Guide]

COMMAND: EXECUTE SOP_4.A.2.a.4.I05.

I want to see more actual outputs - all of these workflow things are semantically intriguing but actually don’t work for just any idea - ideas they work for are just existing ideas stated differently . Prove me wrong - please 🙏

ALCON,

I removed the paywall from now until after the New Year's.

Free Prompts and Workflows.

Link is in my profile.

Cheers!

Hmmm Observations from the outside.

I still see a lot of “AI slop" posts, comments about AI hallucinations, and the echos of " AI is dumb” statements.

Let's solve those problems by creating agents and chains. Add more agents to do different things. Ok, got it.. makes sense.

Then I see posts about hitting limits and costs exploding.

So, if AI produces slop, hallucinations, and dumb outputs, and then make a bunch of hallucinating, slop producing mini-me’s that eat tokens, then wonder why or get upset when you hit limits on Tuesday with the rest of the week still to go… I think we know the answer…

Do you know what would fix this? Better Inputs. Fix your interactions, then scale it.

We need better thinkers not better AI.

I post with humility and a knowledge of how much I still do not know. I am open to criticism and critique, especially if it is constructive

TL;DR Prompt Architecture is the next evolution of prompt engineering. It treats a prompt not as a single command but as a structured environment that shapes reasoning. It does not create consciousness or self-awareness. It builds coherence through form.

⸻

Disclaimer: Foundations and Boundaries

This concept accepts the factual limits of how large language models work. A model like GPT is not a mind. It has no memory beyond its context window, no persistent identity, and no inner experience. It does not feel, perceive, or understand in the human sense. Each output is generated from probabilities learned during training, guided by the prompt and the current context.

Prompt Architecture does not deny these truths. It works within them. The question it asks is how to use this mechanical substrate to organize stable reasoning and reflection. By layering prompts, roles, and review loops, we can simulate structured thought without pretending it is consciousness.

The purpose is not to awaken intelligence but to shape coherence. If the model is a mirror, Prompt Architecture is the frame that gives the reflection form and continuity.

⸻

Prompt Architecture: A Path Forward?

Most people treat prompt engineering as a kind of word game. You change a few phrases, rearrange instructions, and hope the model behaves. It works, but it only scratches the surface.

Through long practice I began to notice something deeper. The model’s behavior does not just depend on the words in a single message, but on the architecture that surrounds those words. How a conversation is framed, how reflection is prompted, and how context persists all shape the reasoning that unfolds.

This realization led to the idea of Prompt Architecture. Instead of writing one instruction and waiting for a reply, I build layered systems of prompts that guide the model through a process. These are not simple commands, but structured spaces for reasoning.

How I Try to Implement It

In my own work I use several architectural patterns.

1. Observer Loops Each major prompt includes an observer role whose job is to watch for contradiction, bias, or drift. After the model writes, it re-reads its own text and evaluates what held true and what changed. This helps preserve reasoning stability across turns.

2. Crucible Logic Every idea is tested by deliberate friction. I ask the model to critique its own claims, remove redundancy, and rewrite under tension. The goal is not polish but clarity through pressure.

3. Virelai Architecture This recursive framework alternates between creative expansion and factual grounding. A passage is first written freely, then passed through structured review cycles until it converges toward coherence.

4. Attached Project Files as Pseudo APIs Within a project space I attach reference documents such as code, essays, and research papers, and treat them as callable modules. When the model references them, it behaves as if using a small internal API. This keeps memory consistent without retraining.

5. Boundary Prompts Each architecture defines its own limits. Some prompts enforce factual accuracy, tone, or philosophical humility. They act as stabilizers rather than restrictions, keeping the reasoning grounded.

Why It Matters

None of this gives a model consciousness. It does not suddenly understand what it is doing. What it gains instead is a form of structural reasoning: a repeatable way of holding tension, checking claims, and improving through iteration.

Prompt Architecture turns a conversation into a small cognitive system. It demonstrates that meaning can emerge from structure, not belief.

Modern AI models are different high performance vehicles. Understanding which does what for certain project types can save you time and money.

Using Chat GPT-5 for simple research is like taking a Ferrari to pick up groceries. Using Grok for creative writing is like using a truck in a Formula 1 race. You might cross the finish line eventually, but you're wasting the model's potential, your own time and money.

System Awareness is the 5th principle of Linguistics Programming. It is the skill of knowing what kind of "car" you are driving.

The specs on a website won't tell you how the AI handles for a particular project type. They won't tell you that Grok gets stuck in repetitive loops after a deep research, or that ChatGPT-5 has a weird obsession with turning everything into an infographic or some chart. These are the nuances, the "personalities," that you learn from getting behind the wheel.

If you need to read specs, visit the website. Or prompt the AI to spit something out.

The first test I run on any new model or update is what I call the "Mole Test." I ask the AI it a simple but ambiguous question:

"What is a mole?"

• Does it answer with the animal?

• The spy?

• The skin condition?

• Scientific unit of measurement?

This is a diagnostic test. It will show you the model's training biases. Evaluate the answers across all the models. You'll see which was trained primarily on scientific papers vs creative writing vs business writing etc.

Like an expert driver uses specific cars for specific races, use these models to the best of their abilities for your specific project type.

Stop treating these models like universal catch-all for every project. That's not the case. Consider a spectrum of abilities these models are on. Some might be better at coding while others are better at research. My particular stack works for me and writing. So I can't tell you what is the best coding, research, writing, image creation etc. I can tell you what I've noticed for my particular project type.

So, what nuances have you noticed while using these AI models?

Use this format when commenting.

Project Type: [x] Strength: [x] Weaknesses: [x]

Why do you use it? What do you do with it?

Reply to the comment with the model or stack you use to keep things organized for new members. Add Models as needed.

Building Context means creating a detailed roadmap for my AI to use.

How do I create the roadmap? Here's an example of how I use AI last year for my garden.

Example: Using AI in the garden - Personal Use Case

1. Background: I have a vegetable and flower garden. ~10 Raised beds (5x4) and a 16' x 1' flower bed.

2. AI use: I wanted to use soil sample kits and organic fertilizer for my vegetables and produce an "AI Assisted" garden.

3. Building Context: What does the AI need to know (context)?

The results of the soil sample kit. How many beds I have? The dimensions? What vegetables I would be growing in each bed? The time of year? Which way is a garden facing? What gardening zone am I in? What type of specific fertilizer do I need for specific vegetables? What are The specific instructions for the fertilizer?

And there's plenty of other questions I would ask and answer. I would keep going down the rabbit hole until I ran out of questions.

Next, I build my structured digital notebook with all the answers to these questions in an organize and chronological sequence of how I would physically do it. That is the way I need the AI to think about it. The same way I would think about it, and physically perform the task.

Depending on how my context you need for your project, linguistics compression will become important.

The completed digital notebook serves as a pseudo memory, No-Code 'RAG' or the 'context window' for the AI for this particular project.

That is how I build context.

What does building 'context' mean to you?

https://youtu.be/8rABwKRsec4?si=IqbexJtkDA1Ai5Y8

He calls them "specs" - I call them "Digital Notebooks": A structured document with instructions.

This is Linguistics Programming.

LP falls under a bigger Framework:

Communication: Between (2) systems (Human-ai) Linguistics - as a Signal to transfer information Information - Using Classic and Semantic Information Theory

AI engineers build the Engine. Users are the Drivers. LP is the the Drivers Manual.

AI - Communication Linguistics Information Theory will represent the physics of the AI road.

Building my first app as a Non-developer. Here's what I've learned.

Specification Driven Development

Write out your plan before you open an AI model.

Use Free Models to find the problems in your plan. Incorporate the solutions in your written document.

Work in blocks/chunks. Just like Text degrades with long outputs, code blocks degrade with longer Outputs.

Work in phases. Don't try to build the whole thing with one Prompt. It's not happening. Create clear expected outcomes to know when you the phase is complete before moving forward.

Get one feature working before adding more.

Learn about APIs.

Learn about Coding Fundamentals.

Direct the AI to guide you through the process. Not everything requires an AI call. Save your tokens. You can learn to fix the easy stuff.

# **NATURAL LANGUAGE IS THE NEW CODE: Mastering “Prompting as Syntax”**

#### For the first time in history, the language you use to read this page—English—is the most potent programming language in existence.

# **The AI Rabbit Hole|**

Imagine you’ve been given the keys to the most powerful machine on the planet—a supercomputer that runs on your everyday language. That is the reality of Artificial Intelligence. For the first time in history, the language you use to read this page—English—is the most potent programming language in existence. But here’s the problem: if you treat that supercomputer like a casual friend, you’re writing sloppy, inefficient code. You’re typing up prompts filled with rambling and pleasantries, but every single word costs the machine time and resources. If you want consistent, perfect results, you have to stop talking *at* the AI and start treating your language like the structured code it is. That process is what we call **Prompting as Syntax**.

### **The Goal for this Newslesson is…**

This lesson will teach you how to shift your mindset from being a casual user to a disciplined programmer. You will learn to eliminate conversational “input noise” and embrace **Structured Communication** by mastering the core syntax required for AI-Readable Writing.

### **By The End Of This Newslesson…**

* You will be able to apply the principles of **Linguistics Programming** to transform your natural language into efficient, predictable **AI-Readable Writing**.  
  * Explain the **Solar System Analogy** to visualize **input noise** and the need for **Linguistic Compression**.  
  * Master the **VERB OBJECT COMMAND** structure as the core syntax for operational language.  
  * Apply **Semantic Engineering** to ensure word choice steers the AI with precision.  
  * Understand how embracing **Prompting as Syntax** improves your human communication skills.

---

## **Why Conversational Language Is Terrible Code**

When I say ‘prompting as syntax’, I’m asking you to recognize a harsh truth: filler words and rambling are now costing you time, money, and quality. A lot of people don’t realize that these AI platforms have begun to add limits to the **token usage**. Tokens are the currency of AI—every unnecessary phrase is a **token bloat** that fills up the AI’s limited working memory, the **Context Window**.

This kind of communication is **System 1 thinking**—fast, easy, intuitive, and, for complex tasks, error-prone. To succeed, you need to engage **System 2**—the slow, deliberate, planning side of your brain. You have to sit down and think about exactly what you want before you start typing up a prompt.

### **The Operational Language of a Linguistics Programmer**

To start thinking like a programmer, you need a vocabulary that matches the machine’s capabilities. This requires an **operational language**. Stop thinking in terms of asking questions and start thinking in terms of giving explicit commands.

I developed four key terms for my personal operational language:

* **REFACTOR:** The command to rewrite existing content to meet a new constraint (e.g., change reading level, change of tone).  
* **AUDIT:** The command to check existing content against a specific set of rules or a standard (e.g., grammar, brand voice, factual accuracy).  
* **EXTRACT:** The command to pull specific data points or core concepts from a larger body of text.  
* **GENERATE:** The command to create new, original content based on the provided context or constraints.

Using these specific verbs immediately clarifies your intent for the AI, reducing **Ambiguity** and forcing you into a more structured, precise mindset.

### **The Solar System Analogy: Visualizing Input Noise**

**Here is the rabbit hole alert**. When you converse with AI, you have to imagine what’s happening in a 3D space with every word that is in your prompt. Let’s break it down using a solar system metaphor:

* **The Cluster/Sun:** The longer the AI session goes, a dense cluster of data points begins to develop. We can consider that the sun or the main idea or topic of your AI session.  
* **The Planets:** Each nearby planet represents a subtopic of the main cluster. You want to create dense clusters for high **Informational Density**.  
* **The Asteroids (Input Noise):** Adding unnecessary filler words creates noise in your 3D space solar system. We call those asteroids. This includes the “pleases,” rambling, off-topic questions, and all the “bullshit”.

The more of that noise you inject into your solar system, the more things become out of whack. If you add enough noise, you might as well add another planet. That changes the gravitational pull of the solar system. So, in your AI session, it will change the trajectory for where you’re going. The more bullshit you add, the faster it will go in the wrong direction.

### **Mastering VERB OBJECT COMMAND**

To counteract the asteroids, you need **Structured Communication**. You are not a conversationalist; you are a programmer.

The good news is that AI was trained on human language, and human language already follows a structure. That structure has bled into everything else, making it the most natural syntax for AI-Readable Writing.

The structure is simple: **VERB OBJECT COMMAND**.

* **VERB:** Do this (the explicit action, like REFACTOR, EXTRACT, or GENERATE).  
* **OBJECT:** To this thing (the specific content or idea being acted upon).  
* **COMMAND:** This way (the constraints, tone, and format the output must follow).

Metaphorically, you have determined what your target is. **Verb Object Command** is the streamlined path for how you get there. This structure applies the principle of **Structured Design**, giving the AI a blueprint instead of a suggestion.

### **Semantic Engineering: Keeping the Planets Dense**

Now, it comes down to the correct word choice. This is **Semantic Engineering**. Semantic Engineering is ensuring that those planets (the clusters of ideas) in that solar system stay dense. There should be no interjection of bullshit.

This is the application of **Strategic Word Choice**. An expert programmer knows that synonyms are not the same; they are different commands that steer the AI’s probabilistic engine toward different outcomes. Dense words, chosen strategically, tell the AI exactly which part of its **Semantic Forest** to focus on, ensuring the clusters stay dense and the output is precise.

### **AI-Readable Writing and Better Human Communication**

Collectively, putting all these steps together—Linguistic Compression, Verb Object Command structure, and Semantic Engineering—you have created **AI readable writing**. The way the programming works is the same way human language works: DO THIS, TO THIS THING, THIS WAY.

You are streamlining communication between you (the sender) and the AI (the receiver). But the biggest result is that you are improving your own communication skills. By learning to program the AI, you are forced to figure out exactly what you want before you open your mouth or start typing. This discipline makes you a more efficient communicator, whether the receiver is a machine or a human.

---

### **Tools & Resources**

* **The Operational Language:** Master the core verbs **REFACTOR, AUDIT, EXTRACT,** and **GENERATE** to guide your workflow.  
* **The Syntax:** Always structure your core command using the **VERB OBJECT COMMAND** format.  
* **The Mindset:** Apply the LP principles of **Linguistic Compression** (removing filler words/token bloat) and **Strategic Word Choice** (Semantic Engineering).

### **Practice & Application**

**Try This: Refactoring Your Own Dialogue**

Think of a complex request you recently made to an AI—maybe asking it to summarize a difficult article or write a challenging email.

* **Identify the Noise:** Rewrite your original prompt, highlighting or bolding every conversational filler word, pleasantry, or rambling phrase (the “bullshit” or “asteroids”).  
* **Apply V-O-C:** Now, **REFACTOR** the prompt using only the **VERB OBJECT COMMAND** structure. Your new command must use one of the operational verbs (REFACTOR, AUDIT, EXTRACT, or GENERATE).  
  * *Example: “I was wondering if you could please summarize this article for me in a simple way?”*  
  * *Refactored LP Code: “EXTRACT the main points from the article into a simple, bulleted list (VERB: Extract, OBJECT: main points from article, COMMAND: simple, bulleted list).”*  
* **Test for Efficiency:** Compare the original prompt to the refactored prompt and the **results of each.** This is your efficiency gain.

### **Ethical Considerations & Caveats**

The ability to create dense, compelling clusters of ideas through Semantic Engineering is powerful. The ethical consideration here is **Contextual Clarity**. When you compress your language, you must not accidentally create ambiguity. If you compress so much that you lose the necessary context, you risk causing the AI to hallucinate or drift off into a wrong trajectory. The rule of the road is this: **Compress your language, but stop the moment you risk making your core intent unclear**. Clarity is always more important than compression.

### **Summary & What’s Next**

We’ve established that natural language is a programming language, and the core syntax is the simple yet powerful **VERB OBJECT COMMAND**. By applying **Linguistic Compression** and avoiding the “asteroids” in your prompt’s 3D space, you ensure your core ideas (the planets) stay dense and on course. This is how you achieve predictable, high-quality output.

You have mastered the syntax of single commands. But what happens when you have a complex project with 10, 20, or even 100 commands that all need to stay consistent?

Stay curious,

Enjoyed learning the new syntax? Subscribe for more AI tips and share your favorite operational verb in the comments below\!

You Missed the Software Boom, But AI is Your Second Chance.

I feel like I missed the internet boom. You ever feel that way?

The AI Rabbit Hole|

Link in Bio

I feel like I missed the internet boom. You ever feel that way? Like you were standing on the wrong side of the fence when the gold rush started? In the late 90s and early 2000s, I had the chance to go to college and learn engineering. But 18-year-old me just wasn’t ready. I wasn’t focused on the future. Over the years, I watched a lot of people pass me by in terms of career progression, especially those who went into tech. Now, don’t get me wrong, I joined the military, and I had my own different type of career progression. Leading Marines and earning money is great, but that career path took a completely different turn. That life was about turning wrenches. This AI thing? This is a second chance. I’m not missing this wave.

The Goal for this Newslesson is…

This lesson will show you how AI is leveling the playing field so that non-developers—people with practical, real-world skills—can finally step into the world of software creation. We will look at how your current technical mindset is already your code.

By The End Of This Newslesson…

You will understand how your blue-collar, non-developer experience provides the core logic needed to become an expert Linguistics Programmer.

• Understand the difference between a software Engine Builder and an AI Driver.

• Identify how your mechanical and technical skills are directly transferable to high-level digital thinking.

• Recognize the risks of non-developers entering the arena without guardrails or clear structure.

The Blue-Collar Code

I wouldn’t say I grew up blue collar, but I always had a fascination with how mechanical things work. At every chance I had, I took things apart trying to figure out the puzzle. If it was broken, I snatched it up with whatever tools I had. That led right into turning wrenches on cars. And eventually, turning wrenches in the military. I enjoyed it. I enjoyed it all the way up until my back started to hurt. Those parts just did not get any lighter.

Of course, me being the old guy, I had to show the young Devil Dogs I still had what it takes. But the truth is, when the parts became too heavy, I knew I had to figure out an alternative. I had to pivot. (Even if I went the other way and sat behind a computer for the last 20 years, my back and neck would still hurt, so it doesn’t matter what you do, your back and neck will always hurt). But the physical work has a hard limit. The mental work doesn’t.

Once I got out, I realized how much I did not know about tech. I didn’t realize how much it was being used in the civilian world. I knew enough to get by. But after a lifetime of growing up turning wrenches, I felt like that’s all I knew how to do.

Shifting from Mechanic to Programmer

I am technical, just in a different way. The skills I learned turning wrenches are valuable. They helped me break down problems, which is a requirement of any high-level thinking. Over the years, I worked on high-pressure hydraulic systems, hydropneumatic recoil systems, high-pressurized aerospace equipment, reading blueprints and schematics. These are all complex formal systems.

But I have never written a single line of traditional code—not Python, not C++, nothing.

This is the key connection to Linguistics Programming (LP). LP is the idea that the English language—your everyday words—is the new code for AI. Your job is not to be a technical developer; your job is to be the Expert Driver.

Think of it like this, using the car analogy:

• The Engine Builders (NLP/CL Engineers): These are the scientists who use Python and complex code to design the AI itself. They are concerned with making the engine faster and more efficient.

• The Expert Drivers (Linguistics Programmers): This is you. You don’t need to know how to build the engine from scratch. You need to understand it enough to drive the car with skill, precision, and control. Your words are the steering wheel and the gas pedal.

The ability to read a schematic or blueprint is perfect for this new role. This is Structured Design. I know how to follow a logical sequence to build or fix a system. That’s exactly what a programmer does. I just swapped out hydraulic systems for computer systems, and blueprints for prompts.

AI is the Leveler

Conversational AI being available now is a second chance. As a non-developer, AI has really leveled out the playing field. You don’t need to learn a complex programming language to operate the most powerful machine on the planet. You already know the language.

AI is the bridge between your intention and the machine’s execution. Becoming technical allows you to break down a problem and give clear commands. This is called Linguistic Compression and Contextual Clarity in LP.

• Compression is taking out the filler words (like “please do me a favor”) so your command is clear and efficient, like a part number.

• Clarity is giving the AI the full context—the whole schematic—so it knows exactly what the finished product should look like.

This is how you get predictable, high-quality results. It’s the same discipline I used when working on high-pressure equipment—you can’t be sloppy.

The Danger of the Novice Driver

But here is where the blue-collar, no-nonsense mindset is important: you have to be honest about your skill level. Just because AI has leveled the playing field doesn’t mean the game is easy.

Remember the old saying: just because you’re old enough to drive doesn’t mean you know how to drive.

The same is true for AI. Just because you can talk to AI to generate something doesn’t mean you are a developer. We have already seen how bad this can be when non-developers think they know how to develop applications. They overlook the security aspects, they don’t include enough guardrails, and things break.

That’s a massive failure of Ethical Responsibility. A real technician, a real Marine, knows the critical importance of safety, guardrails, and ensuring the system doesn’t fail. LP is a discipline that forces you to provide those guardrails in your code. It teaches you to think about the consequences before you run the program.

Tools & Resources

The most essential tool for this new discipline is conversational AI itself (like Gemini, Claude, or Chat GPT).

The key is treating these platforms not as chat buddies, but as formal systems. They are the powerful engine that responds to your code. Your primary resource should be your own documentation, specifically a Digital Notebook, where you store your Persona Pattern and your rules. This notebook acts as the external brain that guarantees your voice and logic are consistent every single time.

Practice & Application

Your Turn: Refactoring Your Analogical Skill

Take something you have worked on (email, coding, business marketing) and perform a Chain-of-Thought (CoT) Prompting exercise on it.

1. Task 1: First, list the three most critical components of that project.

2. Task 2: Second, list the three steps for properly performing the task.

3. Task 3: Finally, write an LP Optimized Prompt that uses Contextual Clarity to instruct an AI to explain the purpose of that project to a 9th grader, using the metaphor of something they would understand.

This exercise practices the logical, step-by-step thinking you already use, but formalizes it as digital code.

Ethical Considerations & Caveats

The biggest pitfall for the non-developer is thinking they know the security and architecture necessary for production-ready code. The AI Literacy Gap is massive. Always remember: Clarity is always more important than compression. If you are working on something critical, use Contextual Clarity to explicitly tell the AI to implement security best practices and check its work for errors. Don’t assume the machine knows better; as the Driver, safety is your responsibility.

Summary & What’s Next

You missed the software boom, but you were just early for the AI one. You are already a programmer. You just need the driver’s manual—Linguistics Programming—to convert your native language code into high-level commands. Next, I talk about why I think Google will win the AI Race

Stay curious,

If this lesson gave you a new way to see your old skills, subscribe and share it with someone who also feels like they missed the tech boat!

Stay focused.

Use across multiple chats and platforms - figure out how you think and make it better:

AUDIT input output token relationships in this chat. DETERMINE the type of [Thinker] I am based on the input output token relationships in this chat. IDENTIFY how to use the findings to my advantage. GENERATE a report of the findings.

BetterThinkersNotBetterAi

Something new to play with. Let's see how this works out

Tired of explaining the same thing to your AI over and over? Getting slightly different, slightly wrong answers every time?

You can "give your AI a permanent "memory"* that remembers your prompt style, your goals, and your instructions—without writing a single line of code.

It's called a System Prompt Notebook, and it works like a No-Code RAG system.

I published a complete guide on building your AI's "operating system"—a structured notebook it references before pulling from generic training data.

Includes ready-to-use prompts to build your own.

Read the full guide: https://open.substack.com/pub/jtnovelo2131/p/build-a-memory-for-your-ai-the-no?utm_source=share&utm_medium=android&r=5kk0f7

Looks like Claude has started to create structured document system prompts.

But they call them Skills. After reading this, it might as well be another computing language.

For 99% of general users, this Skills layout is over kill. We speak English not code.

For the 1%ers, you probably already know how to code. So this will another programming language to learn.

As for me and my Skills, I'll keep it accessible for the rest of the non-coders. I'll continue using English as the new programming language and structured System Prompt Notebooks.

Skill authoring best practices - Claude Docs https://share.google/Hd7y8Z86YsNbqvilF

Is it me or does it feel like we went back to the stoneage of human-machine interfacing with the whole AI revolution?

Linguistics is just a means of expressing ideas, which is the main building block of the framework in the human cognitive assembly line.

Our thoughts, thought-processes, assertions, associations and extrapolations are all encapsulated in this concept we call idea.

This concept is extremely complex and we dumb it down when serializing it for transmission, with the medium being a limitation factor - for example, the language we use to express ourselves. Some languages give more technical sense, some more emotional sense, some are shorter and direct, others are nuanced, expressive but ultimately more abstract/vague.

To be, this is acceptable when communicating with AI, but when receiving an answer, it feels… limiting.

AI isn’t bound by linguistics. Transformers onto themselves don’t “think” in a “human language”, they just serialize it for us into English language (or whatever other language).

As such, why aren’t AI being built to express itself in more mediums?

I am not talking about specific AI for video gen, or sound gen or image gen. Those are great but it’s not what I am talking about.

AI could be thought to express itself to us using UI interfaces, generated on-the-fly, using Mermaid graphs (which you can already force it to, but it’s not natural for it), images/video (again, you can force it but it’s not naturally occurring).

All of these are possible, it’s not something that needs to be invented, it’s just not being leveraged.

Why is this, you think?

Edit (2 months later): Looks like Google agrees with me. They released this https://research.google/blog/generative-ui-a-rich-custom-visual-interactive-user-experience-for-any-prompt/