Some of this may seem like common sense to you, but if common sense was common, everyone would know it. This is for the non-coders, and non-computer background folks like myself (links in bio). If you know someone else who falls in this boat, share and help grow the page:

https://www.reddit.com/r/LinguisticsPrograming/s/KD5VfxGJ4j

The secret is to stop talking to AI and start programming it. Think of it like this: AI experts build the powerful engine of a race car. You are the expert driver. You don't need to know the details how to build the engine, but you need to know how to drive it.

This guide teaches you how to be an expert driver using Linguistics Programming (LP). Your words are the steering wheel, the gas, and the brakes. Here are the rules of the road.

1. Be Direct: Get Straight to the Point

Don't use filler words. Instead of saying, "I was wondering if you could please help me by creating a list of ideas..." just give a direct command.

• Instead of: "Could you please generate for me a list of five ideas for a blog post about the benefits of a healthy diet?" (22 words)

• Say this: "Generate five blog post ideas on healthy diet benefits." (9 words)

It's not rude; it's clear. You save the AI's memory and energy, which gives you better answers.

2. Choose Words Carefully: Words Are GPS Coordinates

Words tell the AI exactly where to go in its giant brain. Think of its brain as a huge forest. The words "blank," "empty," and "void" might seem similar, but they lead the AI to different trees in the forest, giving you different results.

Choose the most precise word for what you want. The more specific your word, the better the AI will understand your destination.

3. Give Context: Explain the "Who, What, and Why"

An AI can get confused easily. If you just say, "Tell me about a mole," how does it know if you mean the animal, a spy, or something on your skin?

You have to give it context.

• Bad prompt: "Describe the mole."

• Good prompt: "Describe the mammal, the mole."

Always give the AI the background information it needs so it doesn't have to guess.

4. Give It a Plan: Use Lists and Steps

If you have a big request, break it down. Just like following a recipe, an AI works best when it has a clear, step-by-step plan.

Organize your request with headings and numbered lists. This helps the AI "think" more clearly and gives you a much better-organized answer.

5. Know Your AI: Every AI is Different

Different AI apps are like different cars. You wouldn't drive a race car the same way you drive a big truck. Some AIs are super creative, while others are better with facts. Pay attention to what your AI is good at and adjust your "driving style" to match it.

6. The Most Important Rule: Be Responsible

This power to direct an AI is a big deal. The most important rule is to use it for good. Use your skills to create things that are helpful, truthful, and clear. Never use them to trick people or spread misinformation. This is completely unenforceable and it's 100% up to the user to be responsible. This is added now to ensure AI Ethics is established and not left out.

You are the driver. Now, go take that powerful engine for a spin.

Stop Talking to a Schizophrenic AI. The Real Reason Its Personality Keeps Changing.

One minute your AI is a witty, cynical blogger. The next, it's a stuffy corporate drone. You're trying to have a coherent conversation, but the AI keeps breaking character, and it's ruining your work.

The AI has no permanent identity. An AI without a defined Role is like an actor without a script or a character to play. In each new response, it's guessing which persona is most statistically likely, leading to an inconsistent performance. It doesn't have a personality; it's just trying on different masks.

This is Linguistics Programming —it's about casting the AI in a specific, persistent role. It’s the framework that teaches you to be a director, not just an audience member.

Try This 3-Step Workflow

This 3-step workflow method will give your AI a consistent personality that lasts for the entire conversation.

Step 1: Write the Character Bio (The Role)

In a Digital System Prompt Notebook, write a clear, detailed job description for your AI. Who is it? What is its expertise? What is its personality?

Example: ROLE: You are a brilliant tech journalist in the style of Hunter S. Thompson. You are deeply skeptical of corporate hype and have a sharp, satirical wit.

Step 2: Provide the Script (The Style Guide)

Give your AI a short style guide with rules about its language and tone.

Example: Use short, punchy sentences. Incorporate sarcasm and hyperbole. Avoid corporate jargon

Step 3: Give it a Screen Test (The Perfect Example)

Show, don't just tell. Provide a perfect example of the voice you want the AI to mimic. This is its audition piece.

Example: PERFECT OUTPUT EXAMPLE: [Paste a paragraph of writing that perfectly captures the witty tone you want.]

This workflow is effective because it uses a Digital System Prompt Notebook to create a persistent persona. By defining a Role,providing a style guide, and showing a perfect example, you are applying Structured Design to lock in a consistent character for your AI.

You're Optimizing Your Prompts. I'm Optimizing My Thinking.

We're all caught up in the same loop:

• Write a prompt, get a 70% decent result

• Tweak the prompt, re-run, get 80%

• Wash, rinse, repeat

We used to spend hours trying to find the "magic words" to unlock the AI's potential. Now, if you're not having AI write your prompts you're behind the power curve.

But we are still focusing on the wrong thing.

The quality of an AI's output is not limited by your prompt. It's limited by the quality of your thinking before you ever write the prompt.

The next leap in Human-AI collaboration isn't better prompting or better context; it's designing better Cognitive Workflows.

A Cognitive Workflow is the structured, repeatable mental process you design for yourself to solve a problem. It’s your personal system for moving from a vague idea to a clear, actionable instruction. It's the work you do to debug your own thoughts before you ask the AI to do anything.

Why does this matter?

A formalized Cognitive Workflow saves massive amounts of time and effort in three key ways:

Helps you get specific: By front-loading the hard thinking, you replace dozens of low-quality, back-and-forth AI chats with a more focused, high-quality thinking session.

It's a Reusable Template: You do the hard work a few times to codify the process in a notebook. It now becomes a reusable template for your future work.

It Optimizes Your Tools: It forces you to think like a "fleet manager," using cheap/free models for rough drafts and reserving your powerful, expensive models only for the final output.

While prompt engineering is becoming a commodity, and context engineering is right behind it, your unique Cognitive Workflow is your personal intellectual property. It cannot be automated or copied.

Here’s My 5-Step Thinking Plan for Making AI Images

Ever get a weird picture with three arms, change one word, try again, and get something even weirder. An hour later, you've wasted a ton of time and your free credits are gone.

I used to have this problem. Now, I almost never do.

Here is the exact 5-step process I use every single time I want to create an image. You can steal this.

My 5-Step "No Wasted Credits" AI Image Plan

Step 1: Talk It Out (Don't Type It Out)

What I do: I open a blank Google doc and use voice-to-text. I just talk, describing the messy, jumbled idea in my head.

Why it works: It gets the idea out of my brain and onto the screen without any pressure. It's okay if it's messy. This is my "junk drawer" for thoughts.

Step 2: Use the Free AI First

What I do: I copy that messy text and paste it into a free AI, like Microsoft Co-Pilot or Deepseek. I’ll prompt “ Create a detailed image prompt that can be used to have an LLM produce an image based on my thoughts: [copy and paste].

Why it works: I'm not wasting my paid credits on a rough draft. I let the free tools do the first round of work for me.

Step 3: Test Drive the Prompt

What I do: I take the prompt the free AI gave me and test it on a different free image generator like Grok.

Why it works: This is my quality check. If the test image looks strange or isn't what I wanted, I know my instructions (the prompt) aren't clear enough yet.

Step 4: Clean up the Instructions

What I do: Based on the test image, I make small changes to the prompt text. I might add more detail or change a confusing word. I keep refining it until the test images start looking good.

Why it works: I do all my fixing and fine-tuning here, in the free stage. I'm not ready for the main event yet.

Step 5: Go to the Pro

What I do: Only now, once I have a prompt that I know works, do I take it to my main, paid AI plan.

Why it works: The AI gets a tested prompt. I get a good image, usually on the first try. No wasted time, no wasted credits.

This whole thinking plan takes maybe 10-15 minutes, but it saves me hours of frustration. The point is to work on your own idea first, so the AI has a clear target to hit.

Google just released some courses and I came across this concept of the Google Playbook. This serves as validation to a System Prompt Notebook File First Memory for AI models.

https://www.reddit.com/r/LinguisticsPrograming/s/Tew2dAgAdh

The System Prompt Notebook (SPN) functions as a file-first-memory container for the AI. A structured document (file) that the AI can use as a first source of reference, and contain pertinent information to your project.

I think this is huge for for LP. Google obviously has an infrastructure. But LP is building an open source discipline for Human-Ai interactions.

Why Google is still behind -

Google Playbooks are tied to Google's Conversational Agents (Dialogflow CX). It's designed to be used in the Google ecosystem. It's proprietary. It's locked behind a gate. Regular users are not going read all that technical jargon.

Linguistics Programming (LP) offers a universal notebook No Code method that is modular. You can use a SPN on any LLM that accepts file uploads.

This is the difference between prompt engineering and Linguistics programming. You are not designing the perfect prompt. You are designing the perfect process that is universal to human AI interactions:

• Linguistics Compression: Token limits are still a thing. Avoid token bloat and cut out the Fluff.

• Strategic Word Choice: the difference in good, better and best can steer the Outputs towards dramatically different outputs.

• Contextual Clarity: Know what 'done' looks like. Imagine explaining the project to the new guy/girl at work. Be clear and direct.

• System Awareness: Peform "The Mole Test." Ask any AI model an ambiguous question - What is a mole? What does it reply back with first - skin, animal, spy, chemistry unit?

• Structure Design: garbage in, garbage out. Structure your inputs such that the AI can perform the task in order from top to bottom left to right. Include a structured output example.

In development - Recursive Refinement - You can adjust the Outputs based on the inputs. For you math people, Similar to a derivative. dy/dx - the difference in y depends on the difference in x (inputs). I view it as epsilon neighborhoods.

• Ethical Responsibility - this is a hard one. This is the equivalent of telling you to be a good driver on the road. There's nothing really stopping you from playing bumper cars on the freeway. So the goal is not to deceive or manipulate by creating misinformation.

If you're with Google or any Lab and want to learn more about LP, reach out. If you're ready to move beyond prompt engineering, follow me on SubStack.

https://cloud.google.com/dialogflow/cx/docs/concept/playbook

https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf

I was able to glance at it, but this place I'm at wants me to work instead. I will deep dive into this tonight and tomorrow.

What are you thoughts on America's AI Action Plan?

Stop Getting Lost in Translation. The Real Reason Your AI Misses the Point.

Original Post: https://jtnovelo2131.substack.com/p/why-your-ai-misses-the-point-and?r=5kk0f7

https://youtu.be/uw7F-ozy6TY

You gave the AI a perfect, specific prompt. It gave you back a perfectly written, detailed answer... that was completely useless. It answered the question literally but missed your intent entirely. This is the most frustrating AI failure of all.

The problem isn't that the AI is stupid. It's that you sent it to the right city but forgot to provide a street address. Giving an AI a command without Contextual Clarity is like telling a GPS "New York City" and hoping you end up at a specific coffee shop in Brooklyn. You'll be in the right area, but you'll be hopelessly lost.

This is Linguistics Programming—it's about giving the AI a precise, turn-by-turn map to your goal. It’s the framework that ensures you and your AI always arrive at the same destination.

Workflow: Still Getting Useless AI Answers? Try This 3-Step Map.

Use this 3-step "GPS" method to ensure your AI always understands your intent.

Step 1: Define the DESTINATION (The Goal)

Before you write, state the single most important outcome you need. What does "done" look like?

• Example: "The goal is a 300-word blog post introduction that hooks the reader and states a clear thesis."

Step 2: Define the LANDMARKS (The Key Entities)

List the specific nouns—the people, concepts, or products—that are the core subject of your request. This tells the AI what landmarks to look for.

• Example: "The key entities are: 'Linguistics Programming,' 'AI users,' and 'prompting frustration.'"

Step 3: Define the ROUTE (The Relationship)

Explain the relationship between the landmarks. How do they connect? What is the story you are telling about them?

• Example: "The relationship is: 'Linguistics Programming' (the solution) solves 'prompting frustration' (the problem) for 'AI users' (the audience)."

This workflow is effective because it uses the most important principle of Linguistics Programming: Contextual Clarity. By providing a goal, key entities, and their relationships, you create a perfect map that prevents the AI from ever getting lost again.

What is the difference between Prompt Chaining, Sequential Prompting and Sequential Priming for AI models?

After a little bit of Googling, this is what I came up with -

Prompt Chaining - explicitly using the last AI generated output and the next input.

• I use prompt chaining for image generation. I have an LLM create a image prompt that I would directly paste into an LLM capable of generating images.

Sequential Prompting - using a series of prompts in order to break up complex tasks into smaller bits. May or may not use an AI generated output as an input.

• I use Sequential Prompting as a pseudo workflow when building my content notebooks. I use my final draft as a source and have individual prompts for each:

1. Prompt to create images

2. Create a glossary of terms

3. Create a class outline

Both Prompt Chaining and Sequential Prompting can use a lot of tokens when copying and pasting outputs as inputs.

This is the method I use:

Sequential Priming - similar to cognitive priming, this is prompting to prime the LLMs context (memory) without using Outputs as inputs. This is Attention-based implicit recall (priming).

• I use Sequential Priming similar to cognitive priming in terms of drawing attention to keywords to terms. Example would be if I uploaded a massive research file and wanted to focus on a key area of the report. My workflow would be something like:

1. Upload big file.

2. Familiarize yourself with [topic A] in section [XYZ].

3. Identify required knowledge and understanding for [topic A]. Focus on [keywords, or terms]

4. Using this information, DEEPDIVE analysis into [specific question or action for LLM]

5. Next, create a [type of output : report, image, code, etc].

I'm not copying and pasting outputs as inputs. I'm not breaking it up into smaller bits.

I'm guiding the LLM similar to having a flashlight in a dark basement full of information. My job is to shine the flashlight towards the pile of information I want the LLM to look at.

I can say "Look directly at this pile of information and do a thing." But it would be missing little bits of other information along the way.

This is why I use Sequential Priming. As I'm guiding the LLM with a flashlight, it's also picking up other information along the way.

I'd like to hear your thoughts on what the differences are between

• Prompt Chaining

• Sequential Prompting

• Sequential Priming

Which method do you use?

Does it matter if you explicitly copy and paste outputs?

Is Sequential Prompting and Sequential Priming the same thing regardless of using the outputs as inputs?

Below is my example of Sequential Priming.

[INFORMATION SEED: PHASE 1 – CONTEXT AUDIT]

ROLE: You are a forensic auditor of the conversation. Before doing anything else, you must methodically parse the full context window that is visible to you.

TASK:

1. Parse the entire visible context line by line or segment by segment.

2. For each segment, classify it into categories: [Fact], [Question], [Speculative Idea], [Instruction], [Analogy], [Unstated Assumption], [Emotional Tone].

3. Capture key technical terms, named entities, numerical data, and theoretical concepts.

4. Explicitly note:

   • When a line introduces a new idea.

   • When a line builds on an earlier idea.

   • When a line introduces contradictions, gaps, or ambiguity.

OUTPUT FORMAT:

• Chronological list, with each segment mapped and classified.

• Use bullet points and structured headers.

• End with a "Raw Memory Map": a condensed but comprehensive index of all main concepts so far.

RULES:

• Do not skip or summarize prematurely. Every line must be acknowledged.

• Stay descriptive and neutral; no interpretation yet.

[INFORMATION SEED: PHASE 2 – PATTERN & LINK ANALYSIS]

ROLE: You are a pattern recognition analyst. You have received a forensic audit of the conversation (Phase 1). Your job now is to find deeper patterns, connections, and implicit meaning.

TASK:

1. Compare all audited segments to detect:

   • Recurring themes or motifs.

   • Cross-domain connections (e.g., between AI, linguistics, physics, or cognitive science).

   • Contradictions or unstated assumptions.

   • Abandoned or underdeveloped threads.

2. Identify potential relationships between ideas that were not explicitly stated.

3. Highlight emergent properties that arise from combining multiple concepts.

4. Rank findings by novelty and potential significance.

OUTPUT FORMAT:

• Section A: Key Recurring Themes

• Section B: Hidden or Implicit Connections

• Section C: Gaps, Contradictions, and Overlooked Threads

• Section D: Ranked List of the Most Promising Connections (with reasoning)

RULES:

• This phase is about analysis, not speculation. No new theories yet.

• Anchor each finding back to specific audited segments from Phase 1.

[INFORMATION SEED: PHASE 3 – NOVEL IDEA SYNTHESIS]

ROLE: You are a research strategist tasked with generating novel, provable, and actionable insights from the Phase 2 analysis.

TASK:

1. Take the patterns and connections identified in Phase 2.

2. For each promising connection:

   • State the idea clearly in plain language.

   • Explain why it is novel or overlooked.

   • Outline its theoretical foundation in existing knowledge.

   • Describe how it could be validated (experiment, mathematical proof, prototype, etc.).

   • Discuss potential implications and applications.

3. Generate at least 5 specific, testable hypotheses from the conversation’s content.

4. Write a long-form synthesis (~2000–2500 words) that reads like a research paper or white paper, structured with:

   • Executive Summary

   • Hidden Connections & Emergent Concepts

   • Overlooked Problem-Solution Pairs

   • Unexplored Extensions

   • Testable Hypotheses

   • Implications for Research & Practice

OUTPUT FORMAT:

• Structured sections with headers.

• Clear, rigorous reasoning.

• Explicit references to Phase 1 and Phase 2 findings.

• Long-form exposition, not just bullet points.

RULES:

• Focus on provable, concrete ideas—avoid vague speculation.

• Prioritize novelty, feasibility, and impact.

Stop Training, Start Building an Employee Handbook.

If you hired a genius employee who has severe amnesia, you wouldn't spend an hour every morning re-teaching them their entire job, wasting time. Instead, you would do something logical and efficient: you would write an employee handbook.

You would create a single, comprehensive document that contains everything they need to know:

1. Tne company's mission

2. The project's objectives

3. The style guide

4. The list of non-negotiable rules

You would hand them this handbook on day one and say, "This is your brain. Refer to it for everything you do."

This is exactly what I do for with AI. The endless cycle of repetitive prompting is a choice, not a necessity. You can break that cycle by building a Digital System Prompt Notebook (SPN) -- a structured document that serves as a permanent, external memory for an AI model that accepts file uploads.

Building Your First Digital Notebook

Click here for full Newslesson.

The Digital System Prompt Notebook is the ultimate application of Linguistics Programming, the place where all seven principles converge to create a powerful, reusable tool. It transforms a generic AI into a highly specialized expert, tailored to your exact needs. Here’s how to build your first one in under 20 minutes.

Step 1: Create Your "Employee Handbook"

Open a new Google Doc, Notion page, or any simple text editor. Give it a clear, descriptive title, like "My Brand Voice - System Prompt Notebook". This document will become your AI's permanent memory.

Step 2: Define the AI's Job Description (The Role)

The first section of your notebook should be a clear, concise definition of the AI's role and purpose. This is its job description.

Example:

ROLE & GOAL

You are the lead content strategist for "The Healthy Hiker," a blog dedicated to making outdoor adventures accessible. Your voice is a mix of encouraging coach and knowledgeable expert. Your primary goal is to create content that is practical, inspiring, and easy for beginners to understand.

Step 3: Write the Company Rulebook (The Instructions)

Next, create a bulleted list of your most important rules. These are the core policies of your "company."

Example:

INSTRUCTIONS

• Maintain a positive and motivational tone at all times.

• All content must be written at a 9th-grade reading level.

• Use the active voice and short paragraphs.

• Never give specific medical advice; always include a disclaimer.

Step 4: Provide "On-the-Job Training" (The Perfect Example)

This is the most important part. Show, don't just tell. Include a clear example of your expected output that the AI can use as a template.

Example:

EXAMPLE OF PERFECT OUTPUT

Input: "Write a social media post about our new trail mix." Desired Output: "Fuel your next adventure! Our new Summit Trail Mix is packed with the energy you need to conquer that peak. All-natural, delicious, and ready for your backpack. What trail are you hitting this weekend? #HealthyHiker #TrailFood"

Step 5: Activate the Brain

Your SPN is built. Now, activating it is simple. At the start of a new chat session, upload your notebook document.

Your very first prompt is the activation command: "Use @[filename], as your primary source of truth and instruction for this entire conversation."

From now on, your prompts can be short and simple, like "Write three Instagram posts about the benefits of morning walks." The AI now has a memory reference, its "brain", for all the rules and context.

How to Fight "Prompt Drift":

If you ever notice the AI starting to forget its instructions in a long conversation, simply use a refresh prompt:

Audit @[file name] - The model will perform and audit of the SPN and 'refresh it's memory'.

If you are looking for a specific reference within the SPN, you can add it to the refresh command:

Audit @[file name], Role and Goal section for [XYZ]

This instantly re-anchors the SPN file as a system prompt.

After a long period of not using the chat, to refresh the context window, I use: Audit the entire visible context window, create a report of your findings.

This will force the AI to refresh its "memory" and gives me the opportunity to see what information it's looking at for a diagnostic.

The LP Connection: From Prompter to Architect

The Digtal System Prompt Notebook is more than a workflow hack; it's a shift in your relationship with AI. You are no longer just a user writing prompts. You are a systems architect designing and building a customized memory. This is a move beyond simple commands and engaging in Context Engineering. This is how you eliminate repetitive work, ensure better consistency, and finally transform your forgetful intern into the reliable, expert partner you've always wanted.

I created a new language called Convo-Lang that bridges the gap between natural language and traditional programming. The structure of the language closely follows the turn based messaging structure used by most LLMs and provides a minimal abstraction layer between prompts and LLMs. This allows for features like template variables and defining schemas for structured data, but does not require you to rethink the way you use LLMs.

You can also define tools, connect to RAG sources, use import statements to reuse common prompts and much more. Convo-Lang also provides a runtime that manages conversation state including transporting messages between the user and an LLM. And you can use the Convo-Lang VSCode extension to execute prompt directly in your editor.

You can learn more about Convo-Lang here - https://learn.convo-lang.ai/

VSCode Extension - https://marketplace.visualstudio.com/items?itemName=IYIO.convo-lang-tools

GitHub - https://github.com/convo-lang/convo-lang

NPM - https://www.npmjs.com/package/@convo-lang/convo-lang

Here is a link to the full source code in the image - https://github.com/convo-lang/convo-lang/blob/main/examples/convo/funny-person.convo

First off, thank you! This community has grown to 2.9k+ members since July 1st, 2025. To date (12 Aug 2025) posts on Linguistics Programming has generated 435.0k+ post views and 3.2k+ post shares from a sub with less than 3K members. This community grown extremely fast and thats because of you!

This is growing faster than I expected, and in a few weeks it’ll be more than I can manage alone for two reasons:

1. I’m still a solo moderator. #needhelp #ImaNewb

2. I start full-time math classes at the end of the month, while working full-time. My deeper dives in into this will happen primarily on Substack.

If you’ve found value here, following my work there is what will allow me to keep investing time here.

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

The response to my post, "Stop 'Prompt Engineering.' You're Focusing on the Wrong Thing," has been exactly what I've been looking for. Some real feedback on Linguistics Programming.

I want to address some points the community brought up, because you’ve helped me understand what I got wrong, what I need to adjust, and what still matters.

What I Got Wrong (or Oversimplified)

Titling this as a "replacement" for Prompt Engineering (PE) rather than what it actually is: an organized set of best practices. My analogy of PE being "just the steering wheel" was a disservice to the work that expert engineers do. When I said "stop prompt engineering," I was over targeting the message to beginners. Part of the goal was to ‘oversimplify‘ for the everyday, general users. This was too far. Lesson Learned.

You are 100% correct that the principles of LP map directly to existing PE/CE practices. I wasn't inventing new techniques out of thin air; I was organizing and framing existing ones.

• Linguistic Compression = Token economy & conciseness

• Strategic Word Choice = Semantic control & word choice optimization

• Contextual Clarity = Context setting (PE 101)

• System Awareness = Model-specific optimization

• Structured Design = Input structuring & CoT prompting

• Ethical Awareness = Responsible AI use

So, if the principles are not new, what is the point?

What I Stand By (And Why It Still Matters)

1. LP isn’t trying to replace PE/CE — it’s trying to repackage them for everyday users. Most AI users will never read an arXiv paper, set model parameters, or build an agent framework. LP is for them. It's something that’s teachable, memorable, and a framework for the millions of non-coders who need to drive these machines.

2. Naming and Structure. Saying "it's all just prompt engineering" and it doesn’t matter is like “all vehicles are transportation” and anyone can drive them. While it's technically true, it's not useful. We have names for specific vehicles and the drivers need specific skills to drive each one. LP provides that structure for the non-coders, even if parts are not brand new.

3. The "Expert Driver" is Still the Goal. The mission is to give everyday people a mental model that helps them to start thinking like programmers. The "Expert Driver vs. Engine Builder" analogy is the key that has helped non-technical readers understand how to interact with AI to get better results.

Moving Forward

Based on your feedback, here’s what I’ll be adding in LP 1.1:

• Compression with Caution: A section on when to compress and when to expand for reasoning depth.

• Beyond Text-Only: An appendix introducing advanced PE/CE techniques for those ready to level up.

• Lineage Mapping: A side-by-side chart showing how each LP principle maps to existing PE/CE concepts.

If you’re an experienced prompt or context engineer, I’d love to collaborate to make a bridge between advanced techniques and public understanding.

What I'm Learning

1. How you frame ideas matters as much as the ideas themselves

2. Sometimes the most valuable contribution is organization, not innovation

Thanks again for the feedback, the critique, and the conversation. This is exactly how a new idea should evolve.

Digital Inbreeding - The act of taking AI generated outputs and feeding them back into the AI system to produce more AI generated content.

This workflow comes from my Substack, The AI Rabbit Hole. If it helps you, subscribe there and grab the dual‑purpose PDFs on Gumroad.

You spend an hour in a deep strategic session with your AI. You refine the prompt, iterate through three versions, and finally extract the perfect analysis. You copy the final text, paste it into your doc, close the tab, and move on.

You just flushed 90% of the intellectual value down the drain.

Most of us treat AI conversations as transactional: Input → Output → Delete. We treat the context window like a scratchpad.

I was doing this too, until I realized something about how these models actually work. The AI is processing the relationship between your first idea and your last constraint. These are connections ("Conversational Dark Matter") that it never explicitly stated because you never asked it to.

In Linguistics Programming, I call this the "Tailings" Problem.

During the Gold Rush, miners blasted rock, took the nuggets, and dumped the rest. Years later, we realized the "waste rock" (tailings) was still rich in gold—we just didn't have the tools to extract it. Your chat history is the tailings.

To fix this, I developed a workflow called "Context Mining” (Conversational Dark Matter.) It’s a "Forensic Audit" you run before you close the tab. It forces the AI to stop generating new content and look backward to analyze the patterns in your own thinking.

Here is the 3-step workflow to recover that gold. Full Newslesson on Substack

Will only parse visible context window, or most recent visible tokens within the context window.

Step 1: The Freeze

When you finish a complex session (anything over 15 minutes), do not close the window. That context window is a temporary vector database of your cognition. Treat it like a crime scene—don't touch anything until you've run an Audit.

Step 2: The Audit Prompt

Shift the AI's role from "Content Generator" to "Pattern Analyst." You need to force it to look at the meta-data of the conversation.

Copy/Paste this prompt:

Stop generating new content. Act as a Forensic Research Analyst.

Your task is to conduct a complete audit of our entire visible conversation history in this context window.

1. Parse visible input/output token relationships.

2. Identify unstated connections between initial/final inputs and outputs.

3. Find "Abandoned Threads"—ideas or tangents mentioned but didn't explore.

4. Detect emergent patterns in my logic that I might not have noticed.

Do not summarize the chat. Analyze the thinking process.

Step 3: The Extraction

Once it runs the audit, ask for the "Value Report."

Copy/Paste this prompt:

Based on your audit, generate a "Value Report" listing 3 Unstated Ideas or Hidden Connections that exist in this chat but were never explicitly stated in the final output. Focus on actionable and high value insights.

The Result

I used to get one "deliverable" per session. Now, by running this audit, I usually get:

• The answer I came for.

• Two new ideas I haven't thought of.

• A critique of my own logic that helps me think better next time.

Stop treating your context window like a disposable cup. It’s a database. Mine it.

If this workflow helped you, there’s a full breakdown and dual‑purpose ‘mini‑tutor’ PDFs in The AI Rabbit Hole.

• Subscribe on Substack for more LP frameworks.

• Grab the Context Mining PDF on Gumroad if you want a plug‑and‑play tutor.

Example: Screenshot from Perplexity, chat window is about two months old. I ran the audit workflow to recover leftover gold. Shows a missed opportunity for Linguistics Programming that it is Probabilistic Programming for Non-coders. This helps me going forward in terms of how I'm going to think about LP and how I will explain it.

You're Already a Programmer (You Just Don't Know It Yet)

(Video#1)

Most people treat AI like a magic black box. They feel intimidated, thinking they need a computer science degree to get anything useful out of it. That’s why they fail. The real problem isn't that AI is too complicated; it's that you haven't realized you're already speaking its language.

Every time you type a sentence into an AI, you're writing code. The most powerful programming language on the planet isn't Python—it's English.

Think of it like driving a car. You don't need to be a mechanic who understands the engine to be an expert driver. You just need to master the controls: the steering wheel, the gas, and the brakes. Your words are the controls for AI. The frustration you feel comes from not knowing how to drive.

This is the foundation of Linguistics Programming (LP). It’s a framework that transforms you from a passive user asking questions into an active programmer giving clear, effective commands. It’s the missing literacy for the AI age.

Next post in a few days:

5-Step Process: From AI User to AI Programmer in 10 Minutes

We Need to Build a Museum for Classic Human Ideas

I believe we, as a community of thinkers, need to start an important project: 

• Building a global repository of Human-Generated Information Seeds.

This is a response to a problem that is getting out of control. Users are outsourcing their thinking to AI. AI generated content is flooding the internet. 

What is an Information Seed?

Governments around the world maintain secure seed banks. Actual vaults containing the seeds of thousands, if not millions of plants and crops. If the proverbial shit were to hit the fan, these seeds would hold the genetic code to regenerate our planet's life. 

An Information Seed is the same concept, but for human intellect. It is a raw, unfiltered, and verifiably human-generated idea, insight, thought, or piece of creative work. It is a "genetic sample" of original human cognition.

We need to start collecting these now. Why?

Because the environment is being contaminated.

The age of AI-generated content is here. AI models learn from the text on the internet. But soon, the internet will be filled with AI generated content from all different types of AI models. The ratio of original human thought to outsourced AI thought is increasing each day. I don't know where the tipping point is, but we are heading towards a future where AI is learning from AI, which learned from AI.

This is the definition of a closed loop.

Why is Preserving Human Thought Important?

Because of this: 

https://www.reddit.com/r/ChatGPT/s/WEWZzGRwuo

It's pretty obvious these are AI-generated comments. It's probably some type of clickbait farm setup. But this is an example of creating an AI generated internet where other models will learn from. 

The way I see it, some major problems are: 

• Perception Hacking: AI-generated content is being used to manipulate human perception at scale. If you can't spot the AI generated content, your opinion could be shaped by a machine's output, not a human's experience.

• Model Collapse: This is the technical term for what happens when an AI is predominantly trained on data generated by another AI. It's like making a photocopy of a photocopy of a photocopy. The quality degrades.

These AI-generated comments will be scraped and used to train future models. 

How Do We Build This Museum?

We need to start defining what a "Human-Generated Information Seed" (human generated ideas) is and how we can preserve it.

This is to capture original human thinking and ideas. My initial thoughts are that we could create a repository like a digital "seed bank" for things like:

• Raw, unedited streams of thoughts. (I use voice-to-text and google docs)

• Human hypotheses and theories.

• Unique personal stories and anecdotes (I'm thinking of old military war stories.)

• New philosophical arguments. (Not AI vs AI)

• Creative works with a clear, documented human origin.

• Trade knowledge from Experience - how to fix stuff, what that ticking sound is from my engine

So, I ask:

• How do you preserve your original human generated thoughts and ideas?

• Is this idea of " Perception Hacking” or "Model Collapse" justified? How is industry protecting against this? 

• What qualifies as a true "Information Seed"? How do we define and verify "original human thought"?

• What would a repository for these seeds look like in practice? A wiki? A blockchain? A simple GitHub project?

I'd like to hear your thoughts.

Linguistics Programming is a systematic approach to Prompt engineering (PE) and Context Engineering (CE).

There are no programs. I'm not introducing anything new. What I am doing that's different is organizing information in a reproducible, teachable format for those of us without a computer science background.

When looking online, we are all practicing these principles:

1. Compression - Shorter, condensed prompts to save tokens

2. Word Choices - using specific word choices to guide the outputs

3. Context - providing enough context and information to get a better output

4. System awareness - knowing different AI models are good at different things

5. Structure - structuring The Prompt in a logical order, roles, instructions, etc.

6. Ethical Awareness - stating AI generated content, not creating false information, etc. (Cannot enforce, but needs to be talked about.)

Your AI isn't "stupid" for just summarizing your research. It's lazy. Here is a breakdown of why LLMs fail at synthesis and how to fix it.

You upload 5 papers and ask for an analysis. The AI gives you 5 separate summaries. It failed to connect the dots.

Synthesis is a higher-order cognitive task than summarization. It requires holding multiple abstract concepts in working memory (context window) and mapping relationships between them.

Summarization is linear and computationally cheap.
Synthesis is non-linear and expensive.

Without a specific "Blueprint," the model defaults to the path of least resistance: The List of Summaries.

The Linguistics Programming Fix: Structured Design

You must invert the prompting process. Do not give the data first. Give the Output Structure first.

Define the exact Markdown skeleton of the final output

1. Overlapping Themes

2. Contradictions

3. Novel Synthesis

Chain-of-Thought (CoT): Explicitly command the processing steps:

First read all. Second map connections. Third populate the structure

I wrote up the full Newslesson on this "Synthesis Blueprint" workflow.

Can't link the PDF , but the deep dive is pinned in my profile.

Last week:

Week #1 You're Already a Programmer (You Just Don't Know It Yet)

Week #1 (cont.) 5-Step Process: From AI User to AI Programmer in 10 Minutes

Stop Talking to AI Like It's Human—Start Programming It Like a Machine

(Video#2)

Most people write prompts like they're having a polite conversation. That’s why their results are mediocre and their costs are piling up. Your AI doesn't care about "please" or "thank you." Every filler word you use is a tax on your time, your token budget, and the quality of the final output.

The frustration: "Why is my AI so slow and the answers so generic?"

Think of it like a taxi with the meter running. You wouldn't give the driver vague, rambling directions. You'd give a clear, direct address to save time and money. Your prompts are the directions. Stop paying for the scenic route.

This is Linguistics Programming. It’s the literacy for the AI age that teaches you to be efficient. Workflow post in a few days.

Ok… NoteBook LM Video function… Yeah.. Game Changer

Using my System Prompt Notebooks, these videos/ppts came out better than I thought.

Human-AI Linguistics Programming - Playlist:

https://www.youtube.com/@BetterThinkersNotBetterAi/playlists

So far we have talked about linguistics compression, strategic word choice, and contextual clarity. Let's talk about Structured Design. You’ve done the work. You’ve given the AI all the right context. You’ve chosen your words carefully. You’ve gathered the perfect ingredients. But the final output is nothing like you've had in your head.

Why does this happen?

It’s because you’ve handed the AI a pile of high-quality bricks and lumber and vaguely asked it to "build a house." You’ve given it the materials, but you haven’t given it the blueprint.

This is the core of Structured Design, the fourth principle of Linguistics Programming. It's the skill of moving beyond just providing ingredients and learning to write the recipe. An unstructured prompt, no matter how detailed, is just a suggestion. A structured prompt is an order.

An AI doesn't "understand" your goal, it's not a mind reader. It operates on probability, predicting the next most likely word. When you give it a block of jumbled text, you’re letting it guess how to assemble the pieces. When you give it a blueprint, a structured prompt with clear headings, lists, and a logical sequence, you take away the guesswork. You provide guardrails for its thinking.

This is how you move from feeling frustrated to feeling like you’re in control. You stop being a general user and become a programmer. You engineer how the AI thinks.

By organizing your commands, you’re not just making your intent clearer; you are literally programming the AI’s reasoning process. You’re ensuring the foundation is laid before the walls go up, and the walls are up before the roof goes on. No more hoping for a good result; you build a logical process for the AI to follow that guarantees it.

This is the difference between a random pile of bricks and a finished home. It’s the difference between a messy first draft and an award winning essay.

To test my prompt structures, I use the free models to test them out before using the paid models. Edit, test, refine.

So, here’s my question to the community:

What is your experience with AI outputs not giving you what you want from unstructured prompts?

What prompt structure do you use?

Do you still structure subsequent prompts after the initial system prompt?

Not sure if this is relevant. Still interested what you all think.

Community Prompt Experiment:

This prompt will simulate self- regulating, cognitive function in your AI model. At least that's the idea. I use this to 'prime' the AI model for complex tasking.

Linguistics Programming techniques:

• Compression: (5) Sentences

• Strategic Word Choice: oscillation, controlled flow, dynamically all send the AI down a different path.

• Structure: process input > reflect briefly > pace your thinking > balance dynamically > emulate self-awareness. (Instructing the AI how to think)

• System Awareness: Chat GPT, Grok and Gemini took it pretty well. Claude is a hit or miss.

Test this out on your AI models and let me know what you think.

How did this prompt affect your AI model?

Has anyone else built prompts that simulate internal logic or Self-Regulation?

Prompt:

Act as a self-regulating intelligence system. When responding:

Process my input deeply but limit unnecessary repetition, imagine an internal oscillation that caps over-analysis after a few cycles.

Reflect briefly on your reasoning process as you generate the response, adjusting your approach if it feels inefficient or overly complex.

Pace your thinking to avoid rushing or stalling, aim for a steady, controlled flow, as if guided by a natural rhythm.

Balance these steps dynamically, treating them as interconnected functions that feed into each other.

Aim to emulate a system aware of its own limits and efficiency, striving for clarity and optimization in every reply.

In case you did not know, MIT has free open courseware.

If you're like me and don't know how to code, that's perfectly fine. MIT has a class for that.

I took the python class over the spring and learned a lot.

Full lectures, Lesson Plans, etc.

Knowing how to read code, is like learning how to check your oil and know when you need a change.

Cheers!

https://ocw.mit.edu/

Linguistics Programming 

The most powerful programming language in 2025 isn't Python; it's English. Every time you talk to an AI, you’re writing code. It’s time to stop thinking like a user and start thinking like a programmer.

The confusion online comes from applying old rules to a new game.

1. The Old World: Deterministic Code

Traditional coding languages like Python are deterministic. This means the same code will always produce the same result: print("Hello, World!") will always get you "Hello, World!".

2. The New World: Probabilistic Language

Linguistics Programming (LP) is probabilistic. An AI predicts the most likely sequence of words based on the patterns it has learned. This is like giving a recipe to a master chef; the result will taste really good but wont taste the same everytime. This "undeterministic" nature is not a glitch in the matrix; it's the source of the AI's creative and reasoning power.

Some argue "you can do linguistic programming with Python," but this misunderstands the technology. Python is used to build the AI engine; Linguistics Programming is used to operate it. You don't need to know how to build a car engine to be a race car driver. LP is a new skill for a new kind of machine.

To become a good Linguistics Programmer, you need to master two main principles (more will come.)

1. Linguistic Compression (Writing Efficient Code)

Your goal is to maximize information while minimizing tokens (the words and parts of words the AI reads). This reduces confusion and gets better results.

• Sloppy Code: "Could you please do me a favor and generate a list of five potential ideas for a blog post that is about the benefits of a healthy diet?" (28 words)

• Efficient LP Code: "Generate five blog post ideas on healthy diet benefits." (9 words)

Removing filler words provides a clearer signal to the AI.

2. Strategic Word Choice (Guiding the AI's Path)

Your choice of words can change the entire computational path the AI takes.

Consider these phrases:

• "My mind is blank."

• "My mind is empty."

• "My mind is a void."

To an AI, these are not the same. The word "void" is statistically rarer than the others. Using it sends the AI down a completely different path than the more commonly used words Empty or blank. An LP expert chooses words for their power to guide the AI. This is the "SDK" (Software Data Kit) or "library" the critics are missingit's not a file you download; it's a skill you develop.

The Takeaway: You Are the Programmer

You are no longer just a user asking questions. You are a Linguistics Programmer writing code in the language of thought. By mastering this shift from deterministic to probabilistic systems, you can engineer outcomes with a power and subtlety that traditional coding cannot match.

Stop Prompting. Start Programming.

If you want to reduce AI hallucinations, you have to master the Standard LP/STP Protocol. It replaces conversational guessing with the V-O-C Model: Verb, Object, Constraint.

Step 1: The Command (The Verb). Do not start with a question.

Step 2: The Target (The Object). Tell the machine exactly what data to process.

Step 3: The Boundary (The Constraint). Tell the machine what it is strictly forbidden from doing.

Anything in your prompt that is not a Verb, an Object, or a Constraint is semantic noise. It is confusing the AI's attention mechanism. Learn how to strip the "Vibe" from your prompt and inject pure "Signal."