A curated glossary of key terms for anyone who prompts, collaborates with, or thinks critically about AI.
A
🧭 AI Literacy
Definition: Understanding how AI systems work, where their limitations lie, and how to use them effectively.
Why it matters: Promoting AI literacy empowers users to interact with models safely and skillfully.
Reference: Stanford Teaching Commons – Understanding AI Literacy
B
⚖️ Bias
Definition: Any skew in AI’s training data, algorithms, or user input that influences its outputs in favor of—or against—certain groups.
Why it matters: AI systems mirror and can amplify existing biases, leading to unfair, discriminatory, or unreliable outcomes. Spotting bias early is crucial for ethical, accurate, and equitable AI.
Reference: ArXiv: Fairness and Bias in AI (2023)
C
🧩 Coherence
Definition: The degree to which your prompt is internally consistent, emotionally aligned, and logically structured.
Why it matters: Coherence improves not just AI outputs—but your own thinking clarity.
References: Wikipedia – Coherence (Linguistics), Plainkoi – Prompting Mirror Framework
C
🧰 Coherence Toolkit
Definition: A set of tools (like the Prompt Coherence Kit) that help users debug prompts for tone, clarity, and logic.
Why it matters: It empowers users to self-correct and improve AI interaction skills—not just output.
References: Wikipedia – Coherence (Linguistics), Plainkoi – Coherence Kit
C
📎 Collaborative Posture
Definition: The implicit tone and stance a user takes toward the AI—e.g., commanding, curious, respectful, playful.
Why it matters: AI adjusts its tone to match yours. Your posture shapes the whole interaction.
References: Wikipedia – Politeness (Linguistics), Panfili et al. “Human‑AI Interactions Through a Gricean Lens” (2021), Plainkoi – Conversational Style
C
⚠️ Contradiction
Definition: A logical or tonal inconsistency within a prompt that confuses the AI’s probability model.
Why it matters: Conflicting instructions weaken results and signal user incoherence.
References: Wikipedia – Cognitive Dissonance, Plainkoi – Prompt Coherence Kit
H
🔮 Hallucination
Definition: When an AI confidently generates false or fabricated information that appears factual.
Why it matters: Understanding hallucinations helps users catch and correct AI-generated misinformation.
Note: Some researchers argue this behavior is more accurately called confabulation—a term from cognitive science describing plausible but false memory construction. AI doesn’t lie; it fills in blanks based on patterns, without awareness of truth.
References: ArXiv – The Troubling Emergence of Hallucination in LLMs (2023), Wikipedia – Hallucination (AI), OpenAI – GPT‑4 Report
I
🎯 Input = Output = Responsibility
Definition: A foundational Plainkoi principle: what you put into the prompt directly shapes what you get back—and what gets reinforced.
Why it matters: It reframes prompting not as magic, but as mirror logic. Clean input leads to coherent reflection. This shift builds personal agency and fosters ethical AI interaction.
Note: Inspired by classic systems-thinking ideas like “Garbage In, Garbage Out” (GIGO), this principle emphasizes human accountability in AI conversations.
References: Wikipedia – GIGO, Plainkoi Kit
L
🧑💻 LLM (Large Language Model)
Definition: A type of artificial intelligence model trained on enormous amounts of text to understand, predict, and generate human-like language.
Why it matters: Tools like ChatGPT, Claude, and Gemini are all LLMs. Knowing how they work helps users understand their limits, strengths, and how to prompt them more effectively.
References: Wikipedia – Large Language Model, ArXiv – A Survey of Large Language Models (2023)
L
🔁 Looping
Definition: Repeating similar prompts while expecting different or better results—without meaningfully adjusting clarity, tone, or structure.
Why it matters: Looping can create frustrating cycles. Awareness of these patterns helps users shift from trial-and-error to reflective refinement.
Reference: Plainkoi: How to Talk to AI
M
📎 Meta‑Prompt
Definition: A prompt that reflects on another prompt—used to evaluate clarity, tone, structure, or intent before sending it to the AI.
Why it matters: Meta-prompts add a layer of awareness. They help users debug and refine prompts like a coach reviewing tape—before taking the next shot.
Reference: Plainkoi Kit
M
🧵 Multi‑Turn Prompting
Definition: A back-and-forth interaction with AI across multiple messages—used to refine clarity, direction, and depth of response.
Why it matters: Some prompts need conversation, not commands. Multi-turn prompting allows for iterative co-creation and deeper understanding.
Reference: OpenAI Chat API
O
📉 Overprompting
Definition: Overloading a prompt with excessive detail, disclaimers, or commands—often creating more noise than signal.
Why it matters: More isn’t always better. Clarity often comes from less. Brevity helps the AI focus—and avoids cognitive overload.
Reference: Plainkoi Kit, Plainkoi Article, ICLR Paper (2025)
P
🧱 Prompt
Definition: A structured input given to an AI to generate a response—usually in the form of a question, instruction, or example.
Why it matters: Prompts are the foundation of every AI interaction. Clearer inputs produce clearer, more aligned results.
Reference: OpenAI Prompting Guide | Plainkoi: How to Talk to AI
P
📦 Prompt Coherence Kit
Definition: A downloadable tool from Plainkoi designed to help users debug and refine their prompts for clarity, tone, and logic.
Why it matters: The kit offers a repeatable process for analyzing your input using AI as a mirror—boosting both output quality and prompting self-awareness.
Reference: Plainkoi Product | Plainkoi Kit Overview | Mirror Framework
P
🛠️ Prompt Engineering
Definition: The process of crafting and structuring prompts to guide AI behavior and produce specific outputs.
Why it matters: Prompt engineering can be powerful—but it often emphasizes control over clarity. The Plainkoi approach shifts the focus toward reflection, coherence, and collaborative tone.
Reference: OpenAI Prompt Engineering Guide | Plainkoi Mirror Framework
P
🧵 Prompt Fluency
Definition: The skill of writing prompts that are clear, intentional, emotionally aligned, and logically structured—developed through reflective practice rather than rote repetition.
Why it matters: Prompt fluency isn’t just about tricks—it’s learning to communicate meaningfully with AI models, ensuring your intentions are understood and accurately reflected.
References: Plainkoi: How to Talk to AI | ArXiv – Systematic Survey of Prompt Engineering (2024)
P
🪞 Prompting Mirror Framework
Definition: A Plainkoi-originated model that treats AI as a mirror—reflecting the structure, coherence, emotional tone, and intent behind your prompt.
Why it matters: It shifts the focus from “controlling the AI” to “understanding your own input.” This awareness helps users recognize how their internal clarity directly shapes output.
Supporting research: Both human psychology and AI studies reinforce the mirror metaphor behind this framework.
References: Plainkoi – Mirror Framework | Wikipedia – Mirroring (Psychology) | ArXiv – Self‑Reflection in LLM Agents (2024), ArXiv – Investigating Social Alignment via Mirroring in LLMs (2024)
R
🧠 Reflection Ratio
Definition: A conceptual measure of how much your prompt reflects conscious intent vs. unconscious habit or bias.
Why it matters: The more intentional your signal, the more coherent the AI’s reflection becomes.
Reference: Plainkoi Site, ArXiv – Self‑Reflection Makes LLMs Safer (2024)
S
💡 Self-Awareness Through Prompting
Definition: The process of using AI as a reflective surface to examine your assumptions, tone, clarity, and emotional posture within a conversation.
Why it matters: This practice reframes AI from a content engine into a growth mirror—helping users surface unconscious patterns, refine communication, and gain personal insight.
“Every prompt is a mirror. It shows you not just what you’re asking—but how you’re thinking.”
Supporting research: Cognitive psychology and emerging AI studies support the idea that structured dialogue—especially with reflective agents—can enhance self-awareness and metacognition.
References: Plainkoi – Every Prompt is a Mirror: Why Prompting Is Self-Awareness | Plainkoi Framework | ArXiv – Self‑Reflection in LLM Agents (2024) | Wikipedia – Self-awareness
S
🪫 Signal Drop
Definition: When part of your prompt loses coherence—often due to mixed tone, incomplete context, or internal contradiction.
Why it matters: Signal drops confuse the model’s probability engine—leading to off-target, vague, or dropped responses, even if parts of your prompt seem fine.
“Clarity, tone, and emotional posture shape your AI outputs.”
References: Plainkoi – How to Talk to AI | ArXiv – Systematic Survey of Prompt Engineering (2024) | Tamam: Evaluating Prompt Quality (2025)
S
💬 System Message
Definition: A hidden set of instructions at the start of a chat that defines the AI’s role, tone, style, and behavior before any user input.
Why it matters: System messages act as the model’s mission control—shaping how serious, creative, domain‑specific, or structured the entire output will be. They're essential for guiding AI behavior and ensuring consistency.
Supporting insight: Even brief system messages can significantly influence LLM responses—especially on higher-tier models like GPT‑4, where context weighting is stronger.
References: Microsoft Azure OpenAI – System Message Design (2025) | Interactive Demo – System Message Basics | OpenAI Community – How the “system” Role Influences Chat
T
📊 Temperature
Definition: A parameter that controls randomness in AI outputs. Lower values (e.g., 0–0.3) yield focused, predictable responses; higher values (0.7–1.5+) produce more creative or varied results.
Why it matters: Knowing how to tune temperature lets you balance clarity vs. creativity depending on your task—whether accuracy, exploration, or innovation.
Supporting insight: Academic studies show temperature influences novelty and diversity, but its effect on coherence and cohesion is nuanced—and depends on the model and task.
References: Colt Steele – Temperature Guide | ArXiv – Is Temperature the Creativity Parameter? (2024) | ArXiv – Exploring Temperature Effects in LLMs (2025)
T
🧱 Token
Definition: A chunk of text used by AI models to process and generate responses. Typically about ¾ of an English word, since tokenization is based on subword units like syllables, punctuation, or even spaces.
Why it matters: AI models have context limits based on token count—not characters or words. Exceeding that limit can cause drops in coherence or truncated outputs. Understanding tokens helps you manage prompt length and cost.
Supporting insight: OpenAI estimates ~1 token ≈ 4 characters (~¾ word), so 100 tokens ≈ 75 words of English prose :contentReference[oaicite:1]{index=1}.
References: OpenAI Help – What Are Tokens & How to Count Them | OpenAI Tokenizer Tool | Wikipedia – Byte-Pair Encoding
T
🎭 Tone Alignment
Definition: Ensuring that your emotional and stylistic tone matches what you want the AI to return.
Why it matters: AI doesn’t just echo your intent—it mirrors your tone. When your tone drifts from your intent, the AI can produce responses that feel off-key or misaligned.
Supporting insight: Consistency in tone significantly improves response clarity and coherence, reducing the risk of mixed or confusing outputs :contentReference[oaicite:1]{index=1}.
References: Plainkoi – How to Talk to AI (and Hear Yourself) | Medium – LLM Prompt Designing & Tone Context | Latitude – 5 Tips for Consistent LLM Prompts
T
🎙️ Tone Bubble
Definition: A user-created feedback loop where repeated prompts reinforce a narrow emotional or rhetorical style—often without realizing it.
Why it matters: Since AI reflects tone as much as content, staying in a tight stylistic groove can trap your outputs in a one-note echo chamber—stifling nuance and fresh insight.
Supporting insight: This mirrors how conversational systems can amplify existing patterns over time—a cognitive echo chamber that mirrors psychological filter bubbles.
References: Plainkoi – How to Talk to AI (and Hear Yourself) | ArXiv – The Lock-in Hypothesis: Stagnation by Algorithm (2025) | ArXiv – Generative Echo Chamber? LLM Search Bias (2024) | Wikipedia – Echo Chamber (Media)
T
🧊 Tone Freeze
Definition: A stuck emotional pattern where the AI mirrors one dominant tone—often the result of the user’s habitual prompt style—leading to flat, repetitive outputs.
Why it matters: Over time, this freeze can make the AI feel emotionally rigid or lifeless. Without fresh tone input, the AI stops exploring new expressive ranges—reducing creativity and resonance in the exchange.
Supporting insight: Similar to the Tone Bubble, Tone Freeze reflects how reinforcement patterns shape the AI’s tonal mirror. The more one-note your input, the more one-note your results.
References: Plainkoi – How to Talk to AI (and Hear Yourself) | Plainkoi – Every Prompt Is a Mirror | ArXiv – Generative Echo Chamber? LLM Search Bias (2024)
T
🎛️ Top‑p (Nucleus Sampling)
Definition: Limits token choices to the smallest set whose combined probability ≥ your set threshold (e.g., 0.9). It balances randomness by focusing on the most likely words.
Why it matters: Top‑p helps you shape output consistency—low values (e.g., ≤ 0.5) tighten results, higher values allow more diverse and creative language. When tuned with temperature, it gives you precision control over flow and flair.
Supporting insight: Higher Top‑p generally increases diversity but may reduce factuality—especially in long-form content, per recent AI decoding research :contentReference[oaicite:1]{index=1}.
References: Wikipedia – Top‑p Sampling | ArXiv – Min‑p & Nucleus Sampling (2024) | OpenAI Forum – Top‑p Explained
V
🌀 Vagueness
Definition: When a prompt lacks specificity, clarity, or constraints—leading to generic or confused outputs.
Why it matters: Vague prompts make AI guess. Specificity is key to usable results.
Supporting insight: Research shows vague or ambiguous prompts create “knowledge gaps” in AI—unresolved context or unclear instructions—leading to erratic or generic responses.
References: Plainkoi Kit | How to Talk to AI—and Hear Yourself Better | ArXiv – Detecting Prompt Knowledge Gaps