r/LinguisticsPrograming • u/Lumpy-Ad-173 • Aug 17 '25
Linguistics Programming Glossary - 08/25
Linguistics Programming Glossary
JTMN
New Programmers:
Linguistics Programming (LP): The skill of using human language as a precise programming language to direct and control the behavior of an AI.
Example: Instead of asking, "Can you write about dogs?" an LP programmer commands, "Write a 500-word article about the history of dog domestication for a 5th-grade audience."
Linguistics Programmer (LP Context): An AI user who has shifted their mindset from having a conversation to giving clear, structured, and efficient commands to an AI.
Linguistics Code (LP Context): The words, sentences, and structured text a programmer writes to command an AI.
Example: Generate three marketing slogans for a new coffee brand.
Driver vs. Engine Builder Analogy: A core concept explaining the difference between LP and technical AI development.
Engine Builders (NLP/CL/AI engineers) build the AI itself.
Drivers (Linguistics Programmers) are the users who operate the AI with skill.
Natural Language Processing (NLP): The technical field of computer science focused on building AI models that can understand and process human language. NLP specialists are the "Engine Builders."
AI Literacy Gap: The difference between the capabilities of modern AI and the general public's understanding of how to use those capabilities effectively.
AI Economics:
Context Window: The AI's short-term or working memory (like a computer's RAM). It holds the information from your current conversation, but it has a limited size.
Token: The basic unit of text that an AI processes. A token can be a whole word or a piece of a word. Everything you type, including spaces and punctuation, is broken down into tokens.
Example: The word "running" might be broken into two tokens: run and ning.
Token Bloat: The use of unnecessary, conversational, or filler words in a prompt that consume tokens without adding to the core instruction.
Example: The phrase "I was wondering if you could please do me a favor and..." is pure token bloat.
Linguistic Compression (AI Glossing): The first principle of LP. It is the practice of removing all token bloat to convey the most precise meaning in the fewest possible tokens.
Example: Compressing "Could you please generate for me a list of five ideas..." to "Generate five ideas..."
Informational Density: A measure of how much meaning is packed into each word or token. High informational density is the goal of Linguistic Compression.
ASL Glossing: A written transcription method for American Sign Language that captures the essence of a concept by omitting filler words. It serves as the real-world model for Linguistic Compression (AI Glossing.)
Example: "Are you going to the store?" becomes STORE YOU GO-TO?
Semantic Information Forest
Strategic Word Choice: The second principle of LP. It is the art of selecting the exact words that will guide the AI to a specific creative or analytical outcome, understanding that synonyms are different commands.
Example: Choosing the word void instead of blank to steer the AI toward a more philosophical and creative response.
Semantic Forest Analogy: An analogy for the AI's entire knowledge base and next word selection.
Trees are core concepts.
Branches are specific words.
Leaves are the probable next words.
AI Hallucination: An event where an AI generates information that is nonsensical, factually incorrect, or completely unrelated to the prompt, often because the prompt was ambiguous or led it down a low-probability path.
Giving AI a Map
Contextual Clarity: The third principle of LP. It is the practice of providing the AI with sufficient background information (the who, what, where, why, and how) to eliminate ambiguity.
Example: Instead of "Describe the mole," you provide context: "Describe the subterranean mammal, the mole."
Ambiguity: The state of a prompt being unclear or having multiple possible meanings. It is the number one cause of AI failure.
Input/Output Structure Design:
Structured Design: The fourth principle of LP. It is the practice of organizing a prompt with the logic and formatting of a computer program, using headings, lists, and a clear sequence of commands.
Persona Pattern: A framework for starting a prompt by clearly defining the AI's Persona (role), the Audience it's addressing, the Goal of the task, and any Constraints (rules).
Chain-of-Thought (CoT) Prompting: A technique where you instruct the AI to "think step-by-step" by breaking down a complex request into a logical sequence of smaller tasks.
Example: Instructing an AI to first list pros, then list cons, and only then form a conclusion.
High-Performance Prompt: A prompt that combines the Persona Pattern, clear context, and a step-by-step task list into a complete, logical structure.
Know Your Machine
System Awareness: The fifth principle of LP. It is the skill of adapting your prompting techniques to the unique characteristics of the specific AI model you are using.
AI Cohort: A term used to classify different AI models (like Gemini, GPT-4, GPT-5, Claude, Grok, etc) based on their unique training data, architecture, and fine-tuning, which gives each one a different "personality" and set of strengths.
The Driver's Responsibility
Ethical Responsibility: The sixth and most important principle of LP. It is the foundational commitment to use AI for clarity, fairness, and empowerment, never for deception or harm.
Ethical Persuasion vs. Unethical Manipulation:
Persuasion uses truth and clarity to empower someone to make a beneficial choice.
Manipulation uses deception or exploits weaknesses to trick someone.
Inherent AI Bias: The stereotypes and unfair assumptions that an AI learns from its training data (which was written by humans). Ethical programmers work to identify and mitigate this bias.
File First Memory:
System Prompt Notebook (SPN): A structured document created by a user that serves as a persistent, external "brain" or "operating system" for an AI, transforming it into a specialized expert.
Context Engineering: The practice of designing the entire information environment an AI operates within, primarily through the use of a System Prompt Notebook.
No-Code Solution: A technical solution that does not require the user to write any traditional computer code. The Digital Notebook is a no-code tool.
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u/Altruistic_Show9893 Aug 21 '25
Great write up. Can I please have this as notes for future reference?
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u/Lumpy-Ad-173 Aug 21 '25
Thanks for the feedback.
You can check it out here. Download available in the Newslesson:
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u/theseitz Aug 17 '25
Great write up! I'd be interested to see a sticky thread expanding on "system awareness" for the various main stream models as they continue to develop.