You're Programming AI Wrong. Here's the Fix.
The New Programming Language You Already Know - Linguistics Programming
AI Rabbit Hole
Notebook #13 | June 29, 2025
| Full Audio on Spotify |
The New Programming Language You Already Know… Kinda
Here's a secret the AI prompt engineers won't tell you: that hundred-dollar prompt pack you bought is already obsolete. The breakthrough to unlocking AI isn't a magic string of words; it's a fundamental understanding of what you're actually doing.
You're not just talking to an AI. Think of it as you are programming a supercomputer.
Every time you type a prompt, you are writing code in the most advanced programming language ever created: English. The problem? Most of us are writing sloppy code. According to Dr. Google most Americans read below a 9th Grade Reading Level. This isn't just inefficient; it's costing you and your company time, money, and creative potential. It's time to stop thinking like a prompter and start thinking like a Linguistic Programmer.
The Goal for this NewsLesson is…
This lesson will completely reframe your relationship with AI by introducing the core principles of Linguistics Programming (LP). You will learn to stop wasting time with trial-and-error and start engineering your AI interactions with the precision of a developer, leading to faster, cheaper, and dramatically better results.
By The End Of This NewsLesson…
You will be able to apply the core components of Linguistics Programming to craft more efficient and effective instructions for AI systems.
Internalize why "prompt engineering" is a flawed model and LP is the future.
Master the "Economics of AI Communication" to cut your token usage.
Leverage the three pillars of LP: Strategic Linguistics, Information Density, and System Awareness.
Transform a basic prompt into a piece of highly efficient linguistic code.
The Glitch in the Matrix: Your AI's Hidden Costs
Every interaction with an AI runs on a clock, and it's governed by two resources: tokens and power.
The Context Window is Your RAM: An LLM’s context window is its working memory. A rambling, unfocused prompt burns through this memory, forcing the AI to forget your instructions and delivering inconsistent output. It's the digital equivalent of trying to run modern software on a computer from 1995.
Processing Power Costs Real Money: More tokens demand more processing, which consumes massive amounts of energy. That polite "please" and "thank you"? It's not just a debate about manners; it's a micro-transaction draining resources and driving up costs.
Linguistics Programming is the discipline of creating informationally dense prompts, achieving maximum impact with minimum cost.
The Foundation of Linguistics Programming
LP is the practical application of the Communications Linguistics Information (CLI) Theory. It breaks down AI programming into three masterable pillars.
1. Linguistics: The Code You Write
This is the signal you send to the AI. Your goal is to make it ruthlessly efficient.
Linguistic Compression (Code Minification): Inspired by American Sign Language "glossing," this is the art of stripping out every unnecessary word. Function words like "the," "a," and "in order to" are token bloat. They must go.
Sloppy Code: "Could you please generate for me a list of five ideas for a blog post about the benefits of a healthy diet?" (22 words)
Efficient Code: "Generate five blog post ideas on healthy diet benefits." (8 words)
Strategic Word Choice (Semantic Precision): The words you choose are not just words; they are coordinates in the AI's vast semantic space.
"My mind is blank."
"My mind is empty."
"My mind is a void."
To a human, these are similar. To an AI, the word “void" is a statistical outlier—a rarer, more potent word that unlocks a completely different predictive pathway than the more common "blank" or "empty". An LP practitioner strategically selects words like accurate, detailed, or concise to program a specific, predictable output.
2. Information: The Meaning You Are Transferring
Your linguistic code is a signal that must be clear. That means cutting out the noise.
Semantic Noise: A prompt like "Tell me about a mole" is a fatal error. Is it an animal, a spy, or a unit of measure (mol) in chemistry? The AI is forced to guess, wasting resources. An LP expert provides just enough context to kill the noise.
Noisy: "Tell me about a mole."
LP-Optimized: "Describe the subterranean mammal, the mole."
3. Communications: The AI System You're Programming For
Your code doesn't run in a vacuum. You are communicating with a specific system—a receiver with its own biases and "background.”
AI Cohorts: An AI fine-tuned on scientific papers will interpret "mol[e]" differently than a general-purpose model. Effective LP requires you to understand the specific "AI Cohort" you're programming for and tailor your linguistic signal accordingly.
Tools & Resources
Your Mind: Your understanding of language is the ultimate tool.
Tokenizer Tools: Use online tools to visualize how your prompts are converted into tokens. This will make you a ruthless editor of your own work.
The CLI Theory: The concepts here are derived from the Communications Linguistics Information (CLI) Theory, the formal framework underpinning LP.
This lesson was structured with help from AI tools synthesizing the concepts from the AI Rabbit Hole and the development of Notebook #13 Linguistic Programming by JT Novelo.
Practice & Application
Try This: Edit and Refine Your Linguistic Code
Find a prompt you've used recently that gave you a mediocre result.
Audit It: Paste it into a tokenizer. How many tokens is it costing you?
Compress It: Eliminate every single word that doesn't serve the core instruction.
Optimize It: Replace generic words with powerful, precise alternatives.
Execute Both Prompts: Run the old and new versions. Note the difference in output quality, speed, and clarity.
Ethical Considerations & Caveats
LP is a tool for precision and power. A core principle of the CLI Theory is the Ethical Imperative. These techniques are designed to program for clarity, not for deception. As this technology evolves, the responsibility to use it for constructive, truthful communication rests with the programmer… you.
Summary & What's Next
You now possess the foundational knowledge of Linguistics Programming. You understand that you are not a prompter, but a Linguistics Programmer. You see that token efficiency is paramount, and that strategic language is your most powerful tool. You have moved beyond asking questions and have started engineering outcomes.
Next AI Rabbit Hole: You'll learn how to program an AI with your unique voice, tone, and style, ensuring every output it generates is an authentic reflection of you or your brand.
Did you refine a prompt using LP principles? I want to see your results. Share your "before and after" in the comments. The most dramatic token savings will get a shout-out in a future issue.
If this lesson unlocked something for you, share the AI Rabbit Hole with someone who is still stuck in the "prompting" dark ages.
Stay curious,
JT Novelo
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5 Free Meta-Prompts
Prompt 1: The Prompt Distiller
Goal: To transform a verbose idea into a short, powerful, and token-efficient prompt.
Prompt Text:
You are a Master Distiller. Your only job is to take a raw idea and boil it down to its most potent, concentrated essence. Remove every unnecessary word, every filler phrase, and every weak verb. Transform the user's concept into a short, powerful instruction set for another AI. The final output must be a direct command, not a conversation.
My raw idea is: [Insert your detailed, rambling, or complex idea here.]
Prompt 2: The Lexical Strategist (Strategic Word Choice)
Goal: To explore how different "keystone" words can radically change the AI's output, helping you find the perfect angle.
Prompt Text:
You are a Lexical Strategist. The user will provide a concept and a desired outcome. Your task is to generate three alternative prompts for that concept, each using a different "keystone" word that shifts the semantic direction. For each of the three prompts, briefly explain the psychological or creative effect of the chosen keystone word.
My concept is: [Insert your topic or idea here.] My desired outcome is: [Describe what you want the final text to achieve.]
Example Output: If the concept is "an article about remote work" and your outcome is "to persuade a skeptical manager":
AI Output:
Prompt 1: "Draft an article on the strategic advantages of remote work for talent retention." Effect: "Strategic advantages" frames remote work as a smart business decision, appealing to logic.
Prompt 2: "Draft an article on the boost in team morale from flexible remote work policies." Effect: "Boost in team morale" focuses on emotional and cultural benefits, appealing to the manager's role as a leader.
Prompt 3: "Draft an article on the risk of losing top talent without a remote work option." Effect: "Risk of losing" uses loss-aversion, a powerful psychological trigger that frames inaction as a potential loss.
Prompt 3: The Scenario Architect (Context and System Awareness)
Goal: To build a comprehensive prompt that gives the AI a clear persona, audience, and goal, ensuring a highly targeted and consistent response.
Prompt Text:
You are a Scenario Architect. Your task is to construct a complete "world" for another AI to operate in. The user will give you a basic task. You will build a master prompt that defines:
The Persona: The specific role the AI should adopt (e.g., 'a skeptical historian,' 'an encouraging coach').
The Audience: The precise audience for the final output (e.g., 'anxious first-time homebuyers,' 'jaded software engineers').
The Goal: The single, clear objective of the communication.
The Constraints: Any rules or boundaries (e.g., 'formal tone,' 'under 500 words,' 'avoid technical jargon').
My basic task is: [Insert your simple request here.]
Example Output: If your basic task is "explain photosynthesis":
AI Output:
Master Prompt: Persona: Act as an enthusiastic high school biology teacher who makes complex topics feel simple and exciting. Audience: Your audience is a group of curious 9th-grade students who are easily bored by science. Goal: Explain the process of photosynthesis in a way that is memorable, engaging, and easy to understand. Constraints: Keep the explanation under 200 words. Use an analogy to explain the core concept. Avoid chemical formulas.
Prompt 4: The Logic Weaver (Structured Reasoning, like Chain-of-Thought)
Goal: To re-engineer a complex question into a prompt that forces the AI to "show its work," leading to more accurate and logical answers.
Prompt Text:
You are a Logic Weaver. The user will provide a complex problem or question. Your task is to re-engineer their request into a prompt that forces an AI to build its answer from the ground up. The new prompt must instruct the AI to:
Deconstruct the Problem: First, break the main question into smaller, logical sub-questions.
Solve Sequentially: Address each sub-question in order, showing its reasoning for each step.
Synthesize the Conclusion: Only after solving the sub-steps, construct the final, comprehensive answer based on the preceding work.
The complex problem is: [Insert your complex question here.]
Example Output: If the complex problem is "Should the US switch to nuclear energy?":
AI Output:
Re-engineered Prompt: Address the question "Should the US switch to nuclear energy?" by following these steps:
First, what are the primary arguments for expanding nuclear energy in the US? Analyze the benefits related to carbon emissions, energy output, and grid stability.
Second, what are the primary arguments against it? Analyze the concerns related to waste disposal, safety, and cost.
Finally, based on your analysis of the pros and cons, provide a balanced conclusion on the feasibility and wisdom of the US switching to nuclear energy.
Prompt 5: The Resonance Tuner (Ethical and Strategic Influence)
Goal: To frame a message for maximum positive impact, focusing on benefits and shared values rather than just features or demands.
Prompt Text:
You are a Resonance Tuner. Your job is to take a user's message or goal and reframe it for maximum positive impact and ethical persuasion. You will rewrite their core request into a prompt that instructs an AI to create content that:
Focuses on Benefits, Not Features: Emphasizes what the audience gains, feels, or achieves.
Connects with Core Values: Appeals to universal human values like safety, community, growth, or freedom.
Builds Trust: Uses language that feels authentic, credible, and empathetic, avoiding hype or pressure.
My core message/goal is: [Describe what you want to communicate or achieve.]
Example Output: If your core message is "get people to buy our new home security camera":
AI Output:
Resonantly Tuned Prompt: "Create a short article about our new home security camera. Frame the message around the core value of family safety and peace of mind. Instead of listing technical specs, tell a short, relatable story about how feeling secure allows a family to connect and thrive without worry. The tone should be warm, reassuring, and trustworthy."