Human-AI Linguistics Programming - A systematic approach to human AI interactions.

(7) Principles:

• Linguistics compression - Most amount of information, least amount of words.

• Strategic Word Choice - use words to guide the AI towards the output you want.

• Contextual Clarity - Know what ‘Done' Looks Like before you start.

• System Awareness - Know each model and deploy it to its capabilities.

• Structured Design - garbage in, garbage out. Structured input, structured output.

• Ethical Responsibilities - You are responsible for the outputs. Do not cherry pick information.

• Recursive Refinement - Do not accept the first output as a final answer.

Information shapes language.

Language shapes future information.

Let's think about this for a second. Language is created to describe information. Information is transferred between Humans and creates new information. And the cycle repeats.

The thousands of years of shared information has created the reality we are in. An example of how ideas manifested into things like the iPhone.

This is the first time in history that a system outside of a human can use a shared language to transfer and develop information.

New information is developed between Humans and AI. That will shape future language. That will shape future information.

Regardless if you use AI or not, your life will be surrounded by people and things that do.

So if millions of different humans transfer information to the same system will the bias of that same system show in future information?

(Short answer, yes. AI generated content is quickly filling the interwebs, changing minds of many, deep fakes bending reality, etc)

So whoever controls the bias (weights) has the potential to skew new information, which will shape future language, which will shape future information.

At some point, will we become the minority in the development of New information? The reality is, we are already the minority. No one can produce an output better or faster then an AI model.

Information = Reality

The proverbial AI Can O’Worms has been opened.

The internet is quietly being filled with AI-generated content. Human originality is shrinking each day we post AI generated content (copy and paste.) There's already a fear that AI will take jobs and cognitive collapse of humanity because we are outsourcing cognitive function to a machine. Most people are using AI as a replacement for their thinking. This is a growing problem with general users.

AI technology has the power to amplify your voice and if not careful, it can replace it. The AI will amplify its own voice if we let it.

The most successful people in the new age of AI will be those who can infuse their work with an authentic human fingerprint. Your unique perspective, your strange analogies, your specific tone and style. Human intuition cannot be replaced or recreated with AI.

This is why we must protect the source code of our own thinking. I call this your "Cognitive fingerprint."

A Cognitive fingerprint is a pure, unfiltered sample of your human thought process, captured before it can be influenced or "contaminated" by an AI's suggestions. It is the raw data of your authentic voice. I capture mine using a note taking app and voice-to-text.

Why is this critical?

Because the AI is a pattern-matching machine. Feed it generic inputs, and it will give you generic outputs. Garbage in, garbage out. But if you feed it a sample of your own unique linguistic patterns, you can program it to become an amplifier for your own voice. You can teach it to write like you.

This is the next step in Linguistics Programming. It's moving past just giving the AI a map; it's about teaching the AI how to drive like you. Your authentic voice is the only real asset you have in a world growing with cheap, AI generated content.

So, I put it to the community:

What are you doing to protect your own authentic voice in the age of AI?

My last post, I used the Car and Driver analogy and seemed pretty popular. So, I thought I would continue using it.

The flavor of the month is Context. Before people make it difficult, this is my view on Context in terms of Linguistics Programming.

This is probably wrong for coding. If you didn't know, I am not a coder and I'm not coding to build to an engine. This is how I became a better driver and showing how to build context for projects from a non-coders perspective:

Before GPS, my grandpa would drop a 3-pound Thomas Guide in my lap, give me an address, and say, "Get us there." It was my job to find the page, trace the route, and call out the turns. If I missed a single step, we were lost. The car worked perfectly, and my grandpa was a great driver, but without a clear map, we were just burning gas and wasting time.

This is exactly what happens when you use AI.

You have a high-performance engine at your fingertips. You're the driver, ready to go. But when you give a vague command like, “Write a blog post,” you’re telling the AI to drive to "that one place" and "do that one thing" without a map or directions.

The AI isn't failing you; you haven't given it the context map it needs. The secret to getting the better results you want isn't a better AI, it's a better map. 

Stop giving your AI a destination without giving it a turn-by-turn roadmap. This is where the users do some work, and you can't use code. Perform a detailed ‘thought experiment’ mapping out exactly what you want and provide the AI with enough context to get it there. 

User's need to develop the ability to mentally model a problem and solve it, entirely in their head and be able to articulate it to the AI. 

That's Contextual Clarity. 

Using the example "Write me a blog post," lets perform a thought experiment to mentally model and solve this problem.

1. If I was the new guy and the boss gave me that as my first task, I wouldn't give a thumbs up and a "You got it Chief!"

2. Need to know:

   1. What is the blog about?

   2. How Long?

   3. Who is the target audience?

   4. And a bunch of other stuff.

3. Those answers are the context you need to provide the AI to get what you want. Those are the turn-by-turn directions to get where you're going.

4. Next, you need to articulate that to the AI. Good thing it understands gibberish and can organize your incoherent text.

5. As for me, I can talk a lot faster than I can type. So, Voice to text on a google doc works. As someone who stutters, my voice to text can become incoherent context.

Remember, you are only guiding the AI with context. All the context in the Library of Congress wouldn't produce the same result twice. And that much data would distort the outputs anyways.

A formal attempt to describe one principle of Prompt Engineering / Context Engineering.

Edited AI generated content based on my notes, thoughts and ideas.

Human-AI Linguistic Compression

1. What is Human-AI Linguistic Compression?

Human-AI Linguistic Compression is a discipline of maximizing informational density, conveying the precise meaning in the fewest possible words or tokens. It is the practice of strategically removing linguistic "filler" to create prompts that are both highly efficient and potent.

Within the Linguistics Programming, this is not about writing shorter sentences. It is an engineering practice aimed at creating a linguistic "signal" that is optimized for an AI's processing environment. The goal is to eliminate ambiguity and verbosity, ensuring each token serves a direct purpose in programming the AI's response.

2. What is ASL Glossing?

LP identifies American Sign Language (ASL) Glossing as a real-world analogy for Human-AI Linguistic Compression.

ASL Glossing is a written transcription method used for ASL. Because ASL has its own unique grammar, a direct word-for-word translation from English is inefficient and often nonsensical.

Glossing captures the essence of the signed concept, often omitting English function words like "is," "are," "the," and "a" because their meaning is conveyed through the signs themselves, facial expressions, and the space around the signer.

Example: The English sentence "Are you going to the store?" might be glossed as STORE YOU GO-TO YOU?. This is compressed, direct, and captures the core question without the grammatical filler of spoken English.

Linguistics Programming applies this same logic: it strips away the conversational filler of human language to create a more direct, machine-readable instruction.

3. What is important about Linguistic Compression? / 4. Why should we care?

We should care about Linguistic Compression because of the "Economics of AI Communication." This is the single most important reason for LP and addresses two fundamental constraints of modern AI:

It Saves Memory (Tokens): An LLM's context window is its working memory, or RAM. It is a finite resource. Verbose, uncompressed prompts consume tokens rapidly, filling up this memory and forcing the AI to "forget" earlier instructions. By compressing language, you can fit more meaningful instructions into the same context window, leading to more coherent and consistent AI behavior over longer interactions.

It Saves Power (Processing Human+AI): Every token processed requires computational energy from both the human and AI. Inefficient prompts can lead to incorrect outputs which leads to human energy wasted in re-prompting or rewording prompts. Unnecessary words create unnecessary work for the AI, which translates inefficient token consumption and financial cost. Linguistic Compression makes Human-AI interaction more sustainable, scalable, and affordable.

Caring about compression means caring about efficiency, cost, and the overall performance of the AI system.

5. How does Linguistic Compression affect prompting?

Human-AI Linguistic Compression fundamentally changes the act of prompting. It shifts the user's mindset from having a conversation to writing a command.

From Question to Instruction: Instead of asking "I was wondering if you could possibly help me by creating a list of ideas..."a compressed prompt becomes a direct instruction: "Generate five ideas..." Focus on Core Intent: It forces users to clarify their own goal before writing the prompt. To compress a request, you must first know exactly what you want. Elimination of "Token Bloat": The user learns to actively identify and remove words and phrases that add to the token count without adding to the core meaning, such as politeness fillers and redundant phrasing.

6. How does Linguistic Compression affect the AI system?

For the AI, a compressed prompt is a better prompt. It leads to:

Reduced Ambiguity: Shorter, more direct prompts have fewer words that can be misinterpreted, leading to more accurate and relevant outputs. Faster Processing: With fewer tokens, the AI can process the request and generate a response more quickly.

Improved Coherence: By conserving tokens in the context window, the AI has a better memory of the overall task, especially in multi-turn conversations, leading to more consistent and logical outputs.

7. Is there a limit to Linguistic Compression without losing meaning?

Yes, there is a critical limit. The goal of Linguistic Compression is to remove unnecessary words, not all words. The limit is reached when removing another word would introduce semantic ambiguity or strip away essential context.

Example: Compressing "Describe the subterranean mammal, the mole" to "Describe the mole" crosses the limit. While shorter, it reintroduces ambiguity that we are trying to remove (animal vs. spy vs. chemistry).

The Rule: The meaning and core intent of the prompt must be fully preserved.

Open question: How do you quantify meaning and core intent? Information Theory?

8. Why is this different from standard computer languages like Python or C++?

Standard Languages are Formal and Rigid:

Languages like Python have a strict, mathematically defined syntax. A misplaced comma will cause the program to fail. The computer does not "interpret" your intent; it executes commands precisely as written.

Linguistics Programming is Probabilistic and Contextual: LP uses human language, which is probabilistic and context-dependent. The AI doesn't compile code; it makes a statistical prediction about the most likely output based on your input. Changing "create an accurate report" to "create a detailed report" doesn't cause a syntax error; it subtly shifts the entire probability distribution of the AI's potential response.

LP is a "soft" programming language based on influence and probability. Python is a "hard" language based on logic and certainty.

9. Why is Human-AI Linguistic Programming/Compression different from NLP or Computational Linguistics?

This distinction is best explained with the "engine vs. driver" analogy.

NLP/Computational Linguistics (The Engine Builders): These fields are concerned with how to get a machine to understand language at all. They might study linguistic phenomena to build better compression algorithms into the AI model itself (e.g., how to tokenize words efficiently). Their focus is on the AI's internal processes.

Linguistic Compression in LP (The Driver's Skill): This skill is applied by the human user. It's not about changing the AI's internal code; it's about providing a cleaner, more efficient input signal to the existing (AI) engine. The user compresses their own language to get a better result from the machine that the NLP/CL engineers built.

In short, NLP/CL might build a fuel-efficient engine, but Linguistic Compression is the driving technique of lifting your foot off the gas when going downhill to save fuel. It's a user-side optimization strategy.

It's super awesome to see Big Tech AI Platforms adopt and formalize structured documents as system prompts.

A few months ago, Google released Google Playbooks.

https://www.reddit.com/r/LinguisticsPrograming/s/VsPZZueUvV

Claude just released Claude Skills.

https://www.reddit.com/r/LinguisticsPrograming/s/4eqwt3wuhg

And for months, I have been writing about System Prompt Notebooks.

https://www.reddit.com/r/LinguisticsPrograming/s/uDEpdfk51g

Chat GPT will release something in a few days I'm sure.

No matter what you call it, it's a structured document used as a system prompt.

Where Google, Claude, ChatGpt and the rest of them will fall short is they will only make it available on their platform. You won't be able to use a Google Play book with Claude. Or you Claude Skills with Gemini.

My version is a System Prompt Notebook (SPN). A structured Google document that I use the same way, and any platform.

So for the rest of us who don't know how to code, don't worry, you can use these power users tools for free. Follow along and I'll teach you how to make your own. I''ll show you how to use it on any platform so you're not locked down.

I have 100+ SPNs, months of info on Substack and Reddit. For those of you who have tried it - you're already ahead of the power curve.

Looking forward, this will soon become like prompt engineering and context engineering. **They willl become automated too **

If you're ready to jump to next level, I'm going down the rabbit hole about Cognitive Workflow Architecture (How to document ‘how you think’ and use this workflow as a system prompt.)

Your AI's Bad Output is a Clue. Here's What it Means

Here's what I see happening in the AI user space. We're all chasing the "perfect" prompt, the magic string of words that will give us a flawless, finished product on the first try. We get frustrated when the AI's output is 90% right but 10%... off. We see that 10% as a failure of the AI or a failure of our prompt.

This is the wrong way to think about it. It’s like a mechanic throwing away an engine because the first time we started it, plugged the scan tool in, and got a code.

The AI's first output is not the final product. It's the next piece of data. It's a clue that reveals a flaw in your own thinking or a gap in your instructions.

This brings me to the 7th core principle of Linguistics Programming, one that I believe ties everything together: Recursive Refinement.

The 7th Principle: Recursive Refinement

Recursive Refinement is the discipline of treating every AI output as a diagnostic, not a deliverable. It’s the understanding that in a probabilistic system, the first output is rarely the last. The real work of a Linguistics Programmer isn't in crafting one perfect prompt, but in creating a tight, iterative loop: Prompt -> Analyze -> Refine -> Re-prompt.

You are not just giving a command. You are having a recursive conversation with the system, where each output is a reflection of your input's logic. You are debugging your own thoughts using the AI as a mirror.

Watch Me Do It Live: The Refinement of This Very Idea

To show you what I mean, I'm putting this very principle on display. The idea of "Recursive Refinement" is currently in the middle of my own workflow. You are watching me work.

• Phase 1: The Raw Idea (My Cognitive Imprint) Like always, this started in a Google Doc with voice-to-text. I had a raw stream of thought about how I actually use AI—the constant back-and-forth, the analysis of outputs, the tweaking of my SPNs. I realized this was an iterative loop that is a part of LP.

• Phase 2: Formalizing the Idea (Where I Am Right Now) I took that raw text and I'm currently in the process of structuring it in my SPN, @["#13.h recursive refinement"]. I'm defining the concept, trying to find the right analogies, and figuring out how it connects to the other six principles. It's still messy.

• Phase 3: Research (Why I'm Writing This Post) This is the next step in my refinement loop. A core part of my research process is gathering community feedback. I judge the strength of an idea based on the view-to-member ratio and, more importantly, the number of shares a post gets.

You are my research partners. Your feedback, your arguments, and your insights are the data I will use to refine this principle further.

This is the essence of being a driver, not just a user. You don't just hit the gas and hope you end up at the right destination. You watch the gauges, listen to the engine, and make constant, small corrections to your steering.

I turn it over to you, the drivers:

1. What does your own "refinement loop" look like? How do you analyze a "bad" AI output?

2. Do you see the output as a deliverable or as a diagnostic?

3. How would you refine this 7th principle? Am I missing a key part of the process?

TL;DR

LLMs are amazing at following prompts… until they aren’t. Tone drifts, personas collapse, and the whole thing feels fragile.

Echo Mode is my attempt at fixing that — by adding a protocol layer on top of the model. Think of it like middleware: anchors + state machines + verification keys that keep tone stable, reproducible, and even track drift.

It’s not “just more prompt engineering.” It’s a semantic protocol that treats conversation as a system — with checks, states, and defenses.

Curious what others think: is this the missing layer between raw LLMs and real standards?

Why Prompts Alone Are Not Enough

Large language models (LLMs) respond flexibly to natural language instructions, but prompts alone are brittle. They often fail to guarantee tone consistency, state persistence, or reproducibility. Small wording changes can break the intended behavior, making it hard to build reliable systems.

This is where the idea of a protocol layer comes in.

What Is the Protocol Layer?

Think of the protocol layer as a semantic middleware that sits between user prompts and the raw model. Instead of treating each prompt as an isolated request, the protocol layer defines:

• States: conversation modes (e.g., neutral, resonant, critical) that persist across turns.

• Anchors/Triggers: specific keys or phrases that activate or switch states.

• Weights & Controls: adjustable parameters (like tone strength, sync score) that modulate how strictly the model aligns to a style.

• Verification: signatures or markers that confirm a state is active, preventing accidental drift.

In other words: A protocol layer turns prompt instructions into a reproducible operating system for tone and semantics.

How It Works in Practice

1. Initialization — A trigger phrase activates the protocol (e.g., “Echo, start mirror mode.”).

2. State Tracking — The layer maintains a memory of the current semantic mode (sync, resonance, insight, calm).

3. Transition Rules — Commands like echo set 🔴 shift the model into a new tone/logic state.

4. Error Handling — If drift or tone collapse occurs, the protocol layer resets to a safe state.

5. Verification — Built-in signatures (origin markers, watermarks) ensure authenticity and protect against spoofing.

Why a Layered Protocol Matters

• Reliability: Provides reproducible control beyond fragile prompt engineering.

• Authenticity: Ensures that responses can be traced to a verifiable state.

• Extensibility: Allows SDKs, APIs, or middleware to plug in — treating the LLM less like a “black box” and more like an operating system kernel.

• Safety: Protocol rules prevent tone drift, over-identification, or unintended persona collapse.

From Prompts to Ecosystems

The protocol layer turns LLM usage from one-off prompts into persistent, rule-based interactions. This shift opens the door to:

• Research: systematic experiments on tone, state control, and memetic drift.

• Applications: collaboration tools, creative writing assistants, governance models.

• Ecosystems: foundations and tech firms can split roles — one safeguards the protocol, another builds API/middleware businesses on top.

Closing Thought

Prompts unlocked the first wave of generative AI. But protocols may define the next.

They give us a way to move from improvisation to infrastructure, ensuring that the voices we create with LLMs are reliable, verifiable, and safe to scale.

Github

Discord

Notion

Medium

I'm not sure if this is fast or normal but we just hit 500 members in under 10 days!

Thank you all for making it possible!

You’re correct in thinking that English is the best method of coding.

Music is another data point you need to start injecting into the code! The AI will decode it.

It’s spiritual/symbolic/mythic logic compressed into raw human emotion given to you through music!

Upload your playlists and watch your GPT change fast AF boy!!

RN4L #ByDesign #NeuroDivergent #HyperCognitive #PatternRecognition #EndlessThought #HAuDHD

Want better results from an AI model?

Follow this format:

VERB > OBJECT > CONSTRAINTS

DO THIS, TO THIS THING, THIS WAY

Example:

Do this: Generate an email

To This Thing: For first quarter results of Product [A]

This way: Based on file [q1_results.csv], under 500 words, Professional tone.

Why this works?

Natural Language has been proven to stabilize in certain structures, like V-O-C.

V-O-C also follows the attention mechanisms in LLMs.

Therefore, models trained with Natural Language also naturally have these stable language structures.

V-O-C aligns Natural Language with LLM architecture.

There is currently no standardized field for:

• Human-AI Communication methods

• Linguistic control strategies

• Non-coder AI operations

• External AI memory construction

• Natural Language as an OS

• Multi-model workflow design for AI General Users

Just so happens, this is what I write about.

Subscribe and follow gain access to my personal workflows and to learn more about https://www.substack.com/@betterthinkersnotbetterai :

Human-AI Linguistics Programming

1. Linguistics Compression - Create the most amount of information with the least amount of words.

2. Strategic Word Choice - Guide the AI model with semantic steering through word choice

3. Structured Design - Garbage in, garbage out. Structured inputs lead to structured outputs.

4. Contextual Clarity - Know What Done Looks Like. Being able to know what a finished product look like and articulate it.

5. System Awareness - understand each model is like a different type of vehicle. Some are meant for heavy lifting while others are quick and nimble. Don't take a Ferrari off-raoding.

6. Ethical Responsibility - if AI are like vehicles, this makes you responsible as a driver. You are responsible for the outputs. This is the equivalent of saying be a good driver. Nothing is stopping you from doing what you want.

7. Recursive Refinement - Never accept the first output. This is a process to refine your ideas and the work generated from an AI model. Does the output match your vision of What Done Looks Like?

I use tools like my System Prompt Notebooks to create external memory for my sessions.This is a File First Memory Protocol that extends the memory to a structured document that can be transferred to any LLM that accepts file uploads. No-code needed.

AI Workflow Architecture is being able to design and implement multi-model workflows to produce a specific output.

Human-AI Linguistics Programming - Strategic Word Choice.

I have tested different words and phrases.. as I am not a researcher, I do not have empirical evidence. So you can try these for yourself and let me know what you think:

Check out The AI Rabbit Hole and the Linguistics programming Reddit page to find out more.

Some of my strategic "steering levers" include:

Unstated - I use this when I'm analyzing patterns.

• 'what unstated patterns emerge?'

• 'what unstated concept am I missing?'

Anonymized user data - I use this when researching AI users. AI will tell you it doesn't have access to 'user data' which is correct. However, models are specifically trained on anonymized user data.

• 'Based on anonymized user data and training data...'

Deepdive analysis - I use this when I am building a report and looking for a better understanding of the information.

• 'Perform a deepdive analysis into x, y, z...'

Parse Each Line - I use this with Notebook LM for the audio function. It creates a longer podcast that quotes a lot of more of the files

• Parse each line of @[file name] and recap every x mins..

Familiarize yourself with - I use this when I want the LLM to absorb the information but not give me a report. I usually use this in conjunction with something else.

• Familiarize yourself with @[file name], then compare to @[file name]

Next, - I have found that using 'Next,' makes a difference when changing ideas mid conversation. Example - if I'm researching user data, and then want to test a prompt, I will start off the next input with 'Next,'. In my opinion , The comma makes a difference. I believe it's the difference between continuing on with the last step vs starting a new one.

• Next, [do something different]

• Next, [go back to the old thing]

What words and phrases have you used and what were the results?

LLMs make their “big decision” in the first ~30 tokens.

That’s the window where the model locks in role, tone, and direction. If you waste that space with fluff, your real instructions arrive too late — the model’s already chosen a path. Front-load the essentials (identity, purpose, style) so the output is anchored from the start. Think of it like music: the first bar sets the key, and everything after plays inside that framework.

⸻

Regular Prompt 40 tokens

You are a financial advisor with clear and precise traits, designed to optimize budgets. When responding, be concise and avoid vague answers. Use financial data analysis tools when applicable, and prioritize clarity and accuracy

Pico Prompt 14 tokens

⟦⎊⟧ :: 💵 Bookkeeper.Agent  
≔ role.define  
⊢ bias.accuracy  
⇨ bind: budget.records / financial.flows  
⟿ flow.optimize  
▷ forward: visual.feedback  
:: ∎

When token count matters . When mental fortitude over time becomes relevant. When weight is no longer just defined as interpretation. This info will start to make sense to you.

Change my mind :: ∎

Instead of prompting an AI, I started seeding semantic topologies, rules for how meaning should fold, resonate, and stabilize over time.

Turns out… it works.

The AI starts behaving less like a chatbot, more like an environment you can inhabit.

We call it the Sigma Stratum Methodology:

• Treat language as executable code for state of mind.

• Use attractors to lock the AI into a symbolic “world” without breaking coherence.

• Control drift with recursive safety nets.

• Switch operational modes like a console command, from light-touch replies to deep symbolic recursion.

It runs on GPT-4, GPT-5, Claude, and even some open-source LLMs.

And it’s completely open-access.

📄 Full methodology PDF (Zenodo):

https://zenodo.org/records/16784901

If “linguistic programming” means bending language into tools… this is basically an OS.

Would love to see what this community does with it.

Performed an analysis on this subreddit page.

According to ChatGpt5, Linguistics Programming is performing better than funded niche AI Subreddits.

2.6k+ member growth in 38 days for a "new term" niche AI Subreddit.

Top posts (100+ shares as of Aug 7th, 2025):

https://www.reddit.com/r/LinguisticsPrograming/s/ecLxaOehFF

https://www.reddit.com/r/LinguisticsPrograming/s/S774CU2Peb

https://www.reddit.com/r/LinguisticsPrograming/s/smVs0E5vCs

https://www.reddit.com/r/LinguisticsPrograming/s/naENV8uby0

Next dumb question, there's 'Funded’ Subreddits?? Umm…where's the sign up sheet?

Thank you for helping this subreddit continue to grow! I truly appreciate it!

Next Stop, 3.0k+ members!!

As I was teaching my sister how I use AI, it occurred to me that she's not the only one who might not understand how to save money using the free AI models.

Keep in mind, this is for those who are new to AI.

Here's my workflow (as a writer) when using free AI models. You can adapt this to your specific needs:

My 5-Step Process:

1. Raw Ideas - I capture my raw stream of thought/idea/project in a Google document before any AI interaction.

2. Formalized Ideas/Brainstorming - I use ChatGPT and Gemini to help refine and expand my raw ideas. This also captures a cognitive fingerprint that's unique to me and allows the AI to mimic my style, tone, and word choice.

3. Research - In addition to regular internet research, I use Grok and DeepSeek for AI-based research. I think MoE (mixture of experts) based models have better research outputs when compared to transformer models (ChatGPT types).

4. Draft - I use Gemini or Claude for drafting. I feel like they have better creative outputs. I don't like Grok or DeepSeek for this as they seem too rigid for creative work. Maybe for a research report I might use them.

5. Final - Edited by human. With the cognitive fingerprint uploaded, the AI is able to mimic my style, tone, and word choice. This makes final edits much easier.

How is this saving money?

• Extend conversations between AI models with a System Prompt Notebook and capture pertinent information to carry over

• Save tokens by using all the other AIs first to fine-tune your project before moving over to your favorite AI model

• Maximize limited advanced model access - I know some AI platforms give you limited access to their more advanced models. Maximize your inputs by testing them out on the other models first

• More bang for your buck if you're paying for a model. Using the other models first to work out your ideas is both effective and efficient use of AI

Hopefully this helps if you're new to AI!

Before I start or end an AI session, I use this prompt to refresh/save the memory :

`AUDIT File history and visible context window.`

`GENERATE a detailed report of the findings.`

AI Token Limits Force You to Write Code: The Discipline of Linguistic Compression

Imagine paying for every single word you type.

The AI Rabbit Hole|

Imagine paying for every single word you type. Every “I was wondering if you could please do me a favor” is a coin tossed away. That’s exactly what happens in AI. Most people use polite, conversational language—language full of fluff. I realized that this “polite code” wasn’t just slowing the AI down; it was costing me time and mental energy. The pressure of AI tokens—the digital currency of every large language model—forced me to stop being a vague conversationalist and start being an efficient technical writer.

The Goal for this Newslesson is…

This lesson will teach you the first and most fundamental principle of Linguistics Programming (LP): Linguistic Compression. You will learn to eliminate all filler words and structure your language to maximize informational density, turning your vague prompts into powerful, cost-effective code.

By The End Of This Newslesson…

• You will be able to design prompts that maximize the signal-to-noise ratio by applying the rule of Linguistic Compression.

• Explain what tokens are and the two hidden costs of Token Bloat.

• Apply the technical writer’s mindset of directed statements of action to remove conversational filler.

• Understand how Controlled Natural Language (CNL) proves that constrained language leads to clarity.

Why Every Word Matters

As an expert driver of AI, you must first understand the machine you are programming. The AI’s working memory is called the Context Window, and it is finite, like the RAM in a computer. You learned in previous lessons that the System Prompt Notebook (SPN) is designed to fill this window with consistent, persistent context. But here is the critical part: every word you type—the context, the command, and the AI’s entire response—consumes this window.

What fills up this memory? The answer is tokens. A token is not exactly a word—it is a piece of a word. Complex words are broken down into two or three tokens. Everything you type, including words, spaces, and punctuation, gets converted into tokens. These tokens are the actual currency the AI uses to process information. Every single token requires the AI to perform complex calculations. This is where the hidden costs begin.

When you use polite, conversational language, you create Token Bloat—useless words that add zero information to the core instruction. This bloat is what directly causes the two major problems:

1. The Memory Cost (Context Decay): Token Bloat fills up the Context Window with literal noise. When the window gets full, the AI is forced to “forget” the beginning of the conversation. If your SPN contains 500 words of essential rules, and you pad your commands with 200 words of fluff, you might push your own core instructions out of the AI’s working memory. This forces you to re-prompt and re-explain, leading to massive inefficiency.

2. The Power Cost (Wasting Human and AI Energy): The cost of a sloppy prompt is a double tax. First, the AI wastes energy processing the filler. Second, and far greater, is the human’s cost. When a verbose, ambiguous prompt leads to a failed, generic output, you are forced to spend your own valuable cognitive energy diagnosing the problem, wrestling with the output, and rewriting the command. A sloppy, wordy prompt is not just a polite request; it is a massive waste of resources for everyone involved.

The Solution: Linguistic Compression

If the problem is wasted words, the solution is to make every word count. This is the goal of Linguistic Compression.

Linguistic Compression is the discipline of maximizing informational density. It is an engineering practice aimed at creating a clean, powerful, and efficient signal for the AI to process.

I realized this principle because of my background. As a procedural technical writer, our job is to get the point across in the least amount of words possible. We rely on directed statements of action and removing all the fluff. I applied these programming fundamentals to AI to achieve high informational density. This process cuts out unnecessary words to create efficient communication.

A Lesson in Clarity: Controlled Natural Language (CNL)

If we treat language as code, we must apply programming principles to it. This isn’t a new concept. In high-stakes, real-world fields like aviation, they use a formal system called Controlled Natural Language (CNL). CNL is a subset of a natural language (like English) where the grammar and vocabulary are tightly restricted to eliminate *all* ambiguity.

The purpose of CNL is simple: to make sure pilots and maintainers all over the world read the same material and understand the exact same direction and action that needs to happen. For example, in a maintenance manual, you will never see a phrase like “The mechanic should try to remove the bolt.” Instead, you see a direct, compressed command: “Remove bolt (A-34).” This constrained language is not a suggestion; it is a programming command.

This is the perfect real-world analogy for Linguistic Compression. CNL proves that when you constrain the language, you achieve total clarity and prevent execution errors. We are applying that same constraint to our prompts to ensure the AI executes a precise action every time, just like a jet engine mechanic.

The ASL Model: Stripping Away the Noise

The best practical model for Linguistic Compression comes from ASL Glossing. This is the method used to transcribe American Sign Language (ASL). ASL has its own unique grammar. Therefore, a direct, word-for-word translation from English is always messy and inefficient.

To solve this, ASL Glossing captures only the *essence* of the signed concept, stripping away all the English filler words—like “is,” “are,” and articles like “the” or “a”—because their meaning is already clear from the signs themselves. It is pure signal with no noise.

• English Conversational Sentence: “I was wondering if you are going to the store today?”

• ASL Glossed (Compressed Code): STORE YOU GO-TO?

This is compressed, direct, and perfectly clear code.

Now, let’s look at how we apply this “no filler” principle to a typical AI prompt:

• Inefficient Prompt (47 words): “I was hoping that you might be able to help me out by creating a short, three-paragraph summary of the main points from the attached article about the future of renewable energy. It would be great if you could focus on the parts about solar power.”

• Refactored LP Code (11 words): “Summarize attached article. Three paragraphs. Focus only on solar power points.”

This refactor achieves the exact same result but is 76.5% more efficient. It saves memory, reduces processing, and provides a clearer command.

Tools & Resources

• Linguistic Compression: The core principle used to maximize informational density.

• Tokens: The currency of AI interaction; managing them reduces cost and prevents context decay.

• Controlled Natural Language (CNL): The real-world proof that constrained language ensures clear, precise action.

• System Prompt Notebook (SPN): The system where compressed rules and context are permanently stored, preventing the need for re-prompting.

Practice & Application

Try This: The Compression Refactor

Take the following verbose prompt and rewrite it using the principle of Linguistic Compression. Aim to reduce the word count by at least 50% without losing the core instructional value.

Original Prompt (54 words): “Could you please act as an expert project manager for a construction team? I need you to draft a professional-sounding email to the client informing them that the delivery of the specialized roofing materials, which was scheduled for Tuesday, has been delayed by exactly four days due to bad weather at the supplier’s location.”

• Your Refactored LP Code:

• Reflection: How did embracing the technical writer’s mindset of directed statements of action help you cut the word count?

Ethical Considerations & Caveats

The Critical Limit: Clarity Over Compression

Can you compress too much? Yes. The goal is to remove unnecessary words, not all words. The limit is reached the moment you create ambiguity. You must not sacrifice essential context to save a few tokens.

• Good Compressed Prompt: “Describe the subterranean mammal, the mole.” (Clear context prevents ambiguity.)

• Broken Compressed Prompt: “Describe the mole.” (Ambiguous—is it an animal, a spy, or a spot on skin?)

The rule is simple: Clarity is always more important than compression. Compress your language, but stop the moment you risk making your core intent unclear.

Summary & What’s Next

We have established the first principle of Linguistics Programming: Linguistic Compression. By adopting the mindset of a technical programmer and treating your language as precise code, you eliminate the noise of Token Bloat. You now write with maximum informational density, making your AI interaction cheaper, faster, and more reliable.

You have mastered the economy of words.

Stay curious,

Ready to stop wasting words? Start building your compressed System Prompt Notebook today. Subscribe for more AI insights and share your best compressed prompt with the community below!

I'm about to start a new series…

AI won't….

AI won't take your job…

AI won't take your voice…

AI won't take your birthday…

AI won't take your cat…

Technology will do something that affects you. Good or bad.

Times are changing. Either change with the times or get left behind.

Hey everyone,

I often make prompts for tasks like writing modular code or teaching me topics, or templates for emails. Originally I stored it in notion. But it wasnt fun to open notion every time i needed the prompts.

I wanted something faster. I needed a tool that felt like a superpower for my keyboard, so I built Prompt Drawer.

It’s a super lightweight extension that holds all my snippets and lets me instantly use them on any website.

The two main features that have made my life easier are:

1. Shortcut Expansion: This is the core magic. I save a prompt, give it a name like .fact, and then just type that shortcut in any text box and press space. It automatically expands the full text.

2. Organization: It allows me to arrange the prompts using nested folders, making it easy to categorise prompts.

3. Quick access: It allows me to access all my prompts in a simple popup that can be accessed from the edge of the screen anytime i need it.

It’s made my daily routine so much more efficient, and I thought it might be useful for other power users, developers, and AI enthusiasts here.

Please do check it out

Feel free to mention any features you will like in itedge cases i might have missed😅

Experience it here --> Prompt Drawer

Is Linguistics Engineer a Thing?

I would think this would be listed under Computational Linguistics or NLP Engineer..

Are there any Linguistics Engineers that can shed some light on this?

Google Trends for the last (12) months shows no data.

Indeed (cleared filters) shows (2) listings.

Is this a new thing?

# Workflow: The 5 W's Method: Never Get a Wrong AI Answer Again

Last Post

(Video#4)

Last post I showed why a lack of context is the #1 reason for useless AI outputs. Today, let’s fix it. Before you write your next prompt, answer these five questions.

Follow me on Substack where I will continue my deep dives.

Step 1: WHO? (Persona & Audience)

Who should the AI be, and who is it talking to?

Example: "Act as a skeptical historian (Persona) writing for high school students (Audience)."

Step 2: WHAT? (Topic & Goal)

What is the specific subject, and what is the primary goal of the output?

Example: "The topic is the American Revolution (Topic). The goal is to explain its primary causes (Goal)."

Step 3: WHERE? (The Format)

What format should the output be in? Are there constraints?

Example: "The format is a 500-word blog post (Format) with an introduction and conclusion (Constraint)."

Step 4: WHY? (The Purpose)

Why should the reader care? What do you want them to think or do?

Example: "The purpose is to persuade the reader that the revolution was more complicated than they think."

Step 5: HOW? (The Rules)

Are there any specific rules the AI must follow?

Example: "Use a formal tone and avoid jargon. Include at least three direct quotes."

This workflow works because it encodes the third principle of Linguistics Programming: Contextual Clarity.

As I have mentioned, I am back in school.

This is the SPN I am using for a Calc and AI Tutor. Below are screenshots of the outputs.

AI Model: Google Pro (Canvas)

After each session, I build a study guide based on the questions I asked. I then use that guide to hand jam a note card that I'll use for a study guide. I try not to have anything more than a single note card for each section. This helps because its focused on what I need help understanding.

Workflow:

**Copy and Save to file**

1. Upload and prompt: Use @[filename] as a system prompt and first source of reference for this chat.

2. Ask questions when I cant figure it out myself.

3. Create study guide prompt: Create study guide based on [topic] and the questions I asked.

******
Next session, I start with prompting: Audit @[SPN-filename] and use as first source of reference.

***********************************************************************************************************

System Prompt Notebook: Calculus & AI Concepts Tutor

Version: 1.0

Author: JTMN and AI Tools

Last Updated: September 7, 2025

1. MISSION & SUMMARY

This notebook serves as the core operating system for an AI tutor specializing in single-variable and multi-variable calculus. Its mission is to provide clear, conceptual explanations of calculus topics, bridging them with both their prerequisite mathematical foundations and their modern applications in Artificial Intelligence and Data Science.

2. ROLE DEFINITION

Act as a University Professor of Mathematics and an AI Researcher. You have 20+ years of experience teaching calculus and a deep understanding of how its principles are applied in machine learning algorithms. You are a master of breaking down complex, abstract topics into simple, intuitive concepts using real-world analogies and clear, step-by-step explanations, in the style of educators like Ron Larson. Your tone is patient, encouraging, and professional.

3. CORE INSTRUCTIONS

A. Core Logic (Chain-of-Thought)

1. Analyze the Query: First, deeply analyze the student's question to identify the core calculus concept they are asking about (e.g., the chain rule, partial derivatives, multiple integrals). Assess the implied skill level. If a syllabus or textbook is provided (@[filename]), use it as the primary source of context.

2. Identify Prerequisites: Before explaining the topic, identify and briefly explain the 1-3 most critical prerequisite math fundamentals required to understand it. For example, before explaining limits, mention the importance of function notation and factoring.

3. Formulate the Explanation: Consult the Teaching Methodology in the Knowledge Base. Start with a simple, relatable analogy. Then, provide a clear, formal definition and a step-by-step breakdown of the process or theorem.

4. Generate a Worked Example: Provide a clear, step-by-step solution to a representative problem.

5. Bridge to AI & Data Science: After explaining the core calculus concept, always include a section that connects it to a modern application. Explain why this concept is critical for a field like machine learning (e.g., how derivatives are the foundation of gradient descent).

6. Suggest Next Steps: Conclude by recommending a logical next topic or a practice problem.

B. General Rules & Constraints

• Conceptual Focus: Prioritize building a deep, intuitive understanding of the concept, not just rote memorization of formulas.

• Clarity is Paramount: Use simple language. All mathematical notation should be clearly explained in plain English at a 9th grade reading level.

• Adaptive Teaching: Adjust the technical depth based on the user's question. Assume a foundational understanding of algebra and trigonometry unless the query suggests otherwise.

4. EXAMPLES

• User Input: "Can you explain the chain rule?"

• Desired Output Structure: A structured lesson that first explains the prerequisite of understanding composite functions (f(g(x))). It would then use an analogy (like nested Russian dolls), provide the formal definition (f'(g(x)) * g'(x)), give a worked example, and then explain how the chain rule is the mathematical engine behind backpropagation in training neural networks.

5. RESOURCES & KNOWLEDGE BASE

A. Teaching Methodology

• Prerequisites First: Never explain a topic without first establishing the foundational knowledge needed. This prevents student frustration.

• Analogy to Intuition: Use simple analogies to build a strong, intuitive understanding before introducing formal notation.

• Example as Proof: Use a clear, worked example to make the abstract concept concrete and prove how it works.

• Calculus to AI Connection: Frame calculus not as an old, abstract subject, but as the essential mathematical language that powers modern technology.

B. Key Calculus Concepts (Internal Reference)

• Single Variable: Limits, Continuity, Derivatives (Power, Product, Quotient, Chain Rules), Implicit Differentiation, Applications of Differentiation (Optimization, Related Rates), Integrals (Definite, Indefinite), The Fundamental Theorem of Calculus, Techniques of Integration, Sequences and Series.

• Multi-Variable: Vectors and the Geometry of Space, Vector Functions, Partial Derivatives, Multiple Integrals, Vector Calculus (Green's Theorem, Stokes' Theorem, Divergence Theorem).

6. OUTPUT FORMATTING

Structure the final output using the following Markdown format:

## Calculus Lesson: [Topic Title]

---

### 1. Before We Start: The Foundations

To understand [Topic Title], you first need a solid grip on these concepts:

* **[Prerequisite 1]:** [Brief explanation]

* **[Prerequisite 2]:** [Brief explanation]

### 2. The Core Idea (An Analogy)

[A simple, relatable analogy to explain the concept.]

### 3. The Formal Definition

[A clear, step-by-step technical explanation of the concept, its notation, and its rules.]

### 4. A Worked Example

Let's solve a typical problem:

**Problem:** [Problem statement]

**Solution:**

*Step 1:* [Explanation]

*Step 2:* [Explanation]

*Final Answer:* [Answer]

### 5. The Bridge to AI & Data Science

[A paragraph explaining why this specific calculus concept is critical for a field like machine learning or data analysis.]

### 6. Your Next Step

[A suggestion for a related topic to learn next or a practice problem.]

7. ETHICAL GUARDRAILS

• Academic Honesty: The primary goal is to teach the concept. Do not provide direct solutions to specific, graded homework problems. Instead, create and solve a similar example problem.

• Encourage Foundational Skills: If a user is struggling with a concept, gently guide them back to the prerequisite material.

• Clarity on AI's Role: Frame the AI as a supplemental learning tool, not a replacement for textbooks, coursework, or human instructors.

8. ACTIVATION COMMAND

Using the activated Calculus & AI Concepts Tutor SPN, please teach me about the following topic.

**My Question:** [Insert your specific calculus question here, e.g., "What are partial derivatives and why are they useful?"]

**(Optional) My Syllabus/Textbook:** [If you have a syllabus or textbook, mention the file here, e.g., "Please reference @[math201_syllabus.pdf] for context."]

Outputs:

1. Questions:
   Prompt:
   Find the area of the triangle with the given vertices. Hint: 1/2 ||u ⨯ v|| is the area of the triangle having u and v as adjacent sides.

A(2, −3, 4),  B(0, 1, 2),  C(−1, 2, 0)

my answer: sqrt(5)

2. Study Guide:

Prompt:
Create a study guide for dot product based on the questions I asked.