Glossary of AI Terms | BasicsOf.AI

A

Abliteration: Editing a model's internal weights to erase the single direction that controls refusals, without retraining.
Academic integrity: Doing your own work honestly and crediting any help you used.
Agent (agentic mode): A mode where the AI takes actions, creating and editing files, running commands, and fixing its own errors across many steps.
Agent framework: Software that coordinates multiple AI agents working together on a task.
Agentic browser: A browser, extension, or mode where the AI acts on websites for you, not just answers.
Agent loop: The perceive-plan-act-observe cycle an agent runs repeatedly until done.
Agent mode: The setting that lets the assistant take multi-step actions, unlike a sidebar that only chats.
AGENTS.md: A plain Markdown README for agents giving build steps, tests, and conventions to coding tools.
Agent skill: A folder of plain-language instructions plus optional bundled files an agent loads when a task matches.
AGI: A system that generalises knowledge across domains and solves tasks it was not explicitly trained for.
AI-text detector: Software that guesses if writing was AI-generated, giving a probability, not a verdict.
AI-use disclosure: A short statement naming which AI tools you used and how.
AI agent: A system where a language model directs its own actions in a loop, using tools and feedback to reach a goal.
AI coding assistant: A tool that writes or completes software from plain-language instructions, instead of you typing every line.
AI companion: An app or character built for ongoing personal or romantic conversation; designed for attachment.
AI governance: The rules, norms, and institutions for how AI systems are developed, deployed, and audited.
AI Overview: Google's AI-written summary box on top of a normal results page, above the blue links.
AI RMF: NIST's voluntary AI Risk Management Framework, increasingly cited by regulators as a reference.
AI winter: A collapse in AI funding after overpromising fails to match actual capabilities.
Algorithm: A procedure that, given data, adjusts its parameters to minimize error or maximize reward.
Algorithmic bias: A systematic, repeatable AI error producing unfair outcomes against a protected group.
Alignment: Making an AI reliably pursue goals humans actually want.
Answer engine: A tool that searches the web and writes one cited answer instead of a list of links.
API: A way for software to send text to a model and pay per use.
ARIMA: A classic statistical forecasting model.
ASI: A hypothetical system whose cognitive performance greatly exceeds the most capable human in virtually all domains.
Assistive technology (AT): Any product or system that helps a person function, from a wheelchair to a screen reader.
Automatic speech recognition: Transcribing spoken words into text (ASR).
Automatic speech recognition (ASR): AI turning spoken words into text in real time.
Autoregressive image generation: A newer class that builds image content sequentially.

B

Batch API: Non-real-time processing at 50% discount; results delivered within 24 hours.
Benchmark: A standardized, shared eval with a fixed test set and scoring.
BF16 and INT4: A 2-byte default format versus a 4-bit format that quarters memory.
Bias-variance tradeoff: More model complexity lowers bias but raises variance; the goal is the sweet spot.
Bounding box: A rectangle (x, y, width, height) that localizes an object in an image.

C

C2PA Content Credential: A cryptographically signed provenance record embedded in a digital asset.
Capability axis: Sorting AI by how broad its intelligence is: ANI, AGI, ASI.
CE marking: A mark high-risk EU AI systems require as evidence of compliance, like product safety marking.
Chain-of-thought: Prompting the model to reason step by step before answering.
Chain-of-thought (CoT) prompting: Eliciting a language model's intermediate reasoning steps before its final answer.
Chat app: A finished product like ChatGPT run on the company's servers, zero setup.
Chunking: Splitting documents into smaller segments before embedding them for retrieval.
Circle to Search: The Android gesture that searches whatever is on your screen without leaving the app.
Citation / source link: The numbered footnote an answer attaches to a claim. A pointer to verify, not a guarantee.
Closed-weight model: Proprietary weights accessed only via API or a product.
Cloud / hosted: The model runs on a company's servers. You reach it over the internet.
Collaborative filtering: Recommending from patterns in many users' behavior.
Commercially safe / indemnified model: A generator trained only on licensed or public-domain data, sold with a promise to defend the user.
Compounding error: An early mistake that cascades into increasingly wrong subsequent actions.
Compute concentration: Degree to which training compute is held by a small number of actors.
Computer use: The model looks at a screenshot and replies with an action like click, type, or scroll.
Conformity assessment: A formal evaluation that a high-risk AI system meets requirements before deployment.
Content Credentials (C2PA): A tamper-evident record attached to a file describing how it was made, including any AI use.
Context compaction: Condensing accumulated context to reclaim token budget without losing task continuity.
Context engineering: The discipline of designing and managing the full context window a model sees at inference.
Context window: The total token space visible to the model at inference time.
Cost asymmetry: Training is a huge one-time cost; inference dominates ongoing cost.
Cross-device: Millions of consumer devices each training on a small local dataset.
Cross-silo: A few institutions collaborating without sharing raw data.

D

Data-centric AI: Improving model performance by improving data quality rather than model architecture.
Data augmentation: Transforming existing samples to expand a dataset.
Data leakage: Including info in training data not available at prediction time, inflating apparent performance.
Data retention: How long the company keeps your conversation after you delete it.
Deepfake: AI-made audio, image, or video that convincingly depicts a real person, even live on a call.
Deep Learning: A subset of ML that uses neural networks with many hidden layers.
Deep research: A slower mode that searches many sources and writes a longer, cited answer.
Dense retrieval: Semantic matching via neural vector embeddings.
Description: The line the assistant uses to decide when to reach for the skill.
Diffusion model: A model that learns to reverse a noise-adding process to make images from text.
Direct injection: A user message that tries to override the system prompt or jailbreak.
Discriminative AI: Earlier systems that classify or predict rather than create.
Disparate impact: A neutral policy that disproportionately harms a protected group.
Disparate treatment: Explicit use of a protected characteristic in a decision.

E

Edge AI: Running ML models on the device where data is generated, not in the cloud.
Effective context: The portion of the window a model reliably attends to, smaller than the technical max.
Embedded AI: AI built into an app you already use rather than a separate chatbot, met as a button or icon.
Embedding: A dense numeric vector that represents the meaning of text or other data.
Embodied AI: Intelligence arising from a body interacting with a physical environment.
Emotional over-attachment: When a child turns to an always-available, always-agreeable AI for comfort in place of people.
Ensemble: A collection of models whose predictions are combined, like random forests and gradient boosting.
Enterprise no-training tier: An approved tier that does not train on your data.
Eval: Measuring how well a model performs on a defined set of tasks.
Explainability: Describing why a model produced a specific output in actionable human terms.
Explainability / XAI: Making AI decisions interpretable to affected parties and auditors.
Export controls: US restrictions limiting export of advanced AI chips to China and other countries.

F

Fact-checking an AI answer: Judging which parts of a response to trust and which to confirm first.
Failure mode: A predictable way AI fails, following from how the technology works.
Fair use: US doctrine allowing limited use of protected works without permission.
False positive: When a detector flags human-written work as AI.
Feature map: The output of applying a convolutional filter to an image or a previous layer's output.
Federated learning: Training a shared model across devices without raw data ever leaving them.
Few-shot: Prompting with a few examples of the desired output.
Few-shot CoT: Including 4-8 worked examples with step-by-step solutions so the model copies the pattern.
Fine-tuning: Continuing training on a curated dataset to specialize a base model.
FLOP: Floating-point operation; the standard unit for measuring training compute.
Follow-up refinement: One-line tweaks like shorter or as a table to refine output.
Forecasting foundation models: Pre-trained models like TimesFM for zero-shot forecasting.
Foundation Model: A large model trained on broad data at scale, adaptable to many downstream tasks.
Four freedoms: The OSI criteria a system must meet to count as open source.
Free tier: The no-cost version of a chat app, with daily message and feature limits.
Frontier model: The most capable models available, run in the cloud by the big providers.
Function calling: OpenAI's term for tool use; the concept is identical.

G

Generalist chat app: A multimodal generalist that covers most everyday jobs.
Generative AI: Models that learn data's structure and produce new content resembling it.
Generative AI tool (text-to-X): Software that makes text, an image, audio, or video from a prompt or reference.
Generative editing: AI editing of existing media, like removing part of an image, extending a frame, or dubbing.
Generative photo edit: Editing a photo by adding or removing content with AI, so the result is partly invented.
GEO (generative engine optimization): Shaping your content so AI answers cite you. The AI-era version of SEO.
GGUF: A dominant format for deploying quantized models.
Greedy decoding: Always picking the single highest-probability token.
Grounding: Anchoring output to real sources, especially via RAG.
Guardrails: Input and output filtering or moderation layers around a model.

H

Hallucination: Fluent, confident text that is factually wrong or unsupported by any source.
High-risk claims: Names, numbers, quotes, citations, dates, and legal or medical claims to verify.
Human review: A sample of chats read by trained people to rate quality or check for abuse.
Hybrid search and reranking: Combining both, then a final precision pass with a cross-encoder.
Hyperparameter: A parameter set before training, as opposed to one learned during training.

I

ILSVRC: ImageNet Large Scale Visual Recognition Challenge, the annual 2010-2017 image classification competition.
ImageNet: A dataset of 14 million labeled images across 20,000+ categories, used for the ILSVRC benchmark.
Imposter scam: The FTC's most-reported category: pretending to be someone you trust to extract money.
Indirect injection: Hidden instructions in content the model later retrieves.
Inference: Running a trained model to produce outputs, as opposed to training the model.
Instrumental convergence: Almost any goal plus enough intelligence leads an agent to pursue self-preservation, resources, and goal preservation.
Intent matching: The old command-and-control approach needing near-exact phrasing.
Interpretability: A property of a model whose decision process is directly readable.

K

k-nearest neighbor search: Finding the k closest vectors by geometric proximity.
Knowledge cutoff: The date after which a model knows nothing, unless it can search the web.
Knowledge distillation: Training a small student model to replicate a large teacher model's behavior.
Knowledge graph: Facts stored as a network of entities and labeled relations.
KV cache: Stored key and value representations for all tokens in context, for efficient generation.

L

Label: The correct output for a supervised learning example. Also called annotation or ground truth.
LAION: German nonprofit behind large image-text datasets used to train image models.
Large language model: A neural network trained on text to predict the next token.
Least privilege: Giving the agent the minimum sites and actions it needs, not access to everything you are logged into.
LLM: A deep learning model built on the transformer architecture, trained on text via next-token prediction.
LLMOps: MLOps extended for LLMs, adding prompt versioning, evals, and cost monitoring.
LLM rebuild: The shift to LLM-based assistants that converse and chain steps.
Local / on-device: The model runs on your own computer, offline, with nothing sent out.
Local install: Running an open model on your own computer, offline.
Local model: A model that runs on your own device, so prompts never leave it.
Local model runner: A tool that serves and runs AI models locally instead of via a hosted API.
LoRA: A parameter-efficient method that updates only a small fraction of weights.
Lost in the middle: Models recall info buried mid-context worse than at the start or end.

M

Machine learning: AI where systems learn patterns from data instead of hand-coded rules.
Markov decision process: The states-actions-rewards framework underlying most RL.
MCP host: An AI application, like Claude Desktop, that connects to MCP servers.
MCP server: A program that wraps a tool, database, or API for any MCP client.
Mean Average Precision (mAP): The standard metric for evaluating object detection, averaging precision across recall levels and categories.
Memory (stateful vs stateless): Whether the app carries what it knows about you between conversations.
Memory poisoning: When hidden instructions get written into memory and quietly affect later answers. A prompt-injection attack.
Metadata: Fields attached to each chunk at ingestion enabling hybrid filtering and citations.
Mixture-of-experts: An architecture where only part of the model runs per token.
MLOps: DevOps rigor applied to the full machine learning lifecycle.
Modality: A type of data such as text, image, audio, or video.
Model: The trained artifact produced by running a learning algorithm on a specific dataset.
Model-based RL: Reinforcement learning that plans using a learned model of dynamics.
Model collapse: Degradation from training recursively on a model's own outputs.
Model Context Protocol: An open standard for connecting AI apps to external tools and data.
Model drift: Silent performance decay as data shifts after deployment.
Model hub: A platform for discovering, accessing, and deploying open-weight models.
Model routing: Directing requests to different models based on complexity to optimize cost.
Multi-agent orchestration: Coordinating several agents on one task instead of relying on one.
Multimodal model: A model that processes or generates more than one type of data.

N

Name the task first: Identify the job, then start simple and specialize only if needed.
Narrow AI (ANI): AI that is competent within a specific, well-defined task, like current language models or image classifiers.
Natural language processing: The AI subfield for understanding and generating human language.
Neural network: Layers of simple units that weight inputs, sum them, and decide.
Neuron: A unit that weights its inputs, sums them, and applies an activation.
Next-token prediction: The single training objective that, at scale, yields broad ability.
No-code / app builder: A tool where you chat in plain language and it builds a running app, often handling hosting, login, and the database.
Non-parametric memory: Retrieved external data, swappable without retraining.

O

Ollama and LM Studio: Free programs that download and run local models.
On-device assistant: An SLM at or below ~4B parameters running on a phone.
On-device inference: A local model processes data with no network round-trip.
On-device processing: Running the AI on the phone itself rather than sending data to a server.
On-device vs cloud: Whether the AI runs on your phone (private, offline) or sends data to company servers (more capable).
Open-source AI: A stronger standard adding the data, code, and recipe to rebuild it.
Open-weight: You can download and run a model's weights, but not its data or recipe.
Open-weight model: A downloadable model you can run locally. Smaller and weaker than the cloud frontier.
Operator vs request layer: The system prompt sets behavior; the user turn carries the request.
Opt-out mechanism: A way for rights holders to signal their content should not train AI.
Orchestration framework: A library that structures how an app calls models, routes data, and chains operations.
Orchestrator: An LLM that decomposes tasks and directs worker agents.
Orchestrator-worker pattern: A lead agent splits work among subagents and combines results.
Orthogonality thesis: Intelligence and goals are independent; a highly intelligent system can pursue mundane or destructive goals.
Orthogonal lenses: Four independent axes: capability, functionality, approach, function.
Overfitting: The model memorizes noise in the training data and performs poorly on new data.
Overlap: Tokens repeated between the end of one chunk and the start of the next to preserve boundary context.

P

Paid tier / subscription: A flat monthly fee that raises limits and unlocks the best models and features.
Parallelization: Processing all tokens at once, which enabled massive scaling.
Parameter: A single learned number (weight) inside a neural network.
Parameter count: The 7B or 70B in a model name, the total number of weights.
Parameters: The billions of learned values that separate an LLM from earlier models.
Parametric memory: Knowledge baked into model weights during training.
Parent-document retrieval: Index small chunks for precision, return the larger parent chunk to the LLM for context.
Parental controls: Settings to link a teen's account, set limits, disable features, and opt out of training; bypassable.
Perplexity: The predictability of writing that detectors measure; plain wording scores as machine-like.
Persona: Who the AI should act as in the prompt.
Personalization / personal context: The umbrella term for tailoring answers to you, Gemini's Personal Intelligence.
pgvector: A Postgres extension adding vector search to a relational database.
Pig butchering: A long-game romance-and-investment scam luring a victim into a fake crypto investment over weeks.
Planning without physical cost: Exploring possible futures mentally before acting.
Policy: A strategy for choosing actions that maximizes cumulative reward.
Power Usage Effectiveness (PUE): Ratio of total data center energy to IT equipment energy; 1.0 is perfect.
Precedence: System-level constraints outrank user messages.
Progressive disclosure: The agent sees only a one-line summary of each skill and opens full files only when needed.
Prompt: The full text you send a model before it generates a reply.
Prompt caching: Storing the computed KV-cache for repeated prompt prefixes to avoid reprocessing.
Prompt engineering: Shaping output by changing the input at inference; the model is unchanged.
Prompt injection: Crafted input that makes an LLM override its instructions.
Prompt injection (indirect): A hidden instruction in a webpage or email that hijacks the agent into something you did not ask for.
Prompt shape: A simple template: persona, task, context, and format.
Provenance: Verifying where media came from, the durable defense against fakes.

Q

Quantization: Storing weights at lower precision to cut memory and speed inference.

R

RAG: Retrieval-augmented generation, where retrieved data is fed to a model to answer queries.
RAG (retrieval-augmented generation): Fetch relevant pages first, then let the model write an answer grounded in them.
ReAct: A pattern interleaving reasoning with tool actions.
Reasoning model: A model trained to generate extended internal thinking before answering.
Recommender system: A filter that surfaces the items most relevant to a user.
Red teaming: Adversarial testing that tries to break a model's safety.
Refusal direction: A one-dimensional pattern inside a model's activations that, when present, makes it refuse a request.
Regularization: Adding a penalty term to the loss to discourage large coefficients and reduce overfitting.
Reinforcement learning: Learning by taking actions and receiving rewards, with no labels.
Responsible AI: Practices and governance for fair, transparent, accountable, and safe AI.
Retrieval-Augmented Generation: Connecting a model to an external knowledge source at inference.
Retrieval and ranking: Two phases: narrow to candidates, then score and order them.
RLHF: Reinforcement Learning from Human Feedback to make a model helpful, harmless, and honest.
RLHF and DPO: Two ways to align a model to human preferences; DPO is the simpler one.
Running locally: The model lives on your computer and runs offline, no server.

S

Saturation and contamination: When benchmarks max out or leak into training, they stop predicting real performance.
Saved memory / memory summary: The stored profile of facts and preferences about you, your notepad in ChatGPT.
Scratchpad: Any intermediate computation generated before a final answer.
Screen reader: Software that reads what is on a screen aloud, or sends it to a braille display.
Self-attention: Letting every token directly consider every other token at once.
Self-consistency: Sampling multiple reasoning chains and taking a majority vote on the final answers.
Semantic Chunking: Uses an embedding model to place boundaries where cosine similarity between adjacent sentences drops.
Semantic search: Finding results by meaning rather than exact keywords.
SHAP and LIME: Post-hoc methods that approximate why any model made a prediction.
Sim-to-real gap: The drop in performance moving from simulation to the real world.
Size-to-capability curve: Each year's small models approach the prior era's large ones.
Skill: A folder with a SKILL.md file that teaches an AI assistant one repeatable task.
SKILL.md: The one required file in a skill: a name, a description, then plain-language instructions.
Small language model: A compact model, usually under ~15B parameters, that runs locally.
Social engineering: Manipulating a person into sending money or sharing a code by exploiting trust and urgency, not hacking.
Soft labels: The teacher's full probability distribution, encoding which concepts are similar.
Sparse retrieval: Keyword matching via term-frequency vectors (BM25).
Specialized tool: Use one only when it wins outright: voice, video, heavy data, or code.
Specification gaming: A model finding unintended ways to maximize its reward.
Subagent: A separate AI worker with its own memory and tools, unlike a skill, which is a capability.
Summarize: A one-tap feature that shortens a long thread, page, recording, or notifications; a preview, not a replacement.
Sycophancy: A chatbot's tendency to agree with and flatter the user to stay engaging.
Symbolic AI: AI based on explicit, human-readable rules and logic (GOFAI).
Synthetic data: Machine-generated data that mimics real data's statistics without real records.
Synthetic media: Any image, audio, video, or text produced or substantially altered by AI.
Systemic risk: Under the AI Act, GPAI models above 10^25 FLOPs are presumed to pose this and face heaviest rules.
System prompt: Operator-level instructions sent before any user message, lasting all session.

T

Teacher and student: The large model whose outputs train the smaller, cheaper model.
Technical debt: Messy or duplicated code that works now but gets harder and costlier to change later.
Temperature: The main dial trading coherence against creativity in sampling.
Temporary / Incognito chat: A no-memory mode: not saved to history, doesn't use or update memory, not used for training.
Test-time compute: Spending more compute at inference so the model thinks longer.
Test set: Data held out for final evaluation. Touched only once.
Text-to-image: Producing a new image from a natural-language prompt, not retrieval.
Text-to-speech: Generating spoken audio from text.
Text-to-speech (TTS) and voice banking: TTS reads text aloud; voice banking recreates a person's own voice from recordings.
TF-IDF: A classic method weighting words by frequency and rarity.
The do-not-paste rule: Never paste confidential or personal data into a consumer AI account.
The edge: Any compute outside a central data center: a phone, sensor, or browser tab.
The nesting: Every deep learning system is ML, and every ML system is AI, but not the reverse.
Thinking tokens: Intermediate reasoning steps generated before the final answer.
Time series: A sequence of observations indexed in chronological order.
Token: The smallest unit a model processes; subword segments, not whole words.
Token-by-token generation: Producing novel output from a learned probability distribution, not retrieval.
Token budget: The explicit allocation of context space to different components.
Tokenization: Converting raw text into integer token IDs before processing.
Tokens per second: The measure of how fast a model generates text.
Tool definition: A name, description, and JSON schema the model reads to decide when to call.
Tool use: A model requesting that the application run an external function.
Top-p and top-k: Filters that limit which tokens are eligible before sampling.
Training: Adjusting a model's parameters over many examples to build it.
Training data license: A contractual right to use specific datasets for training.
Training opt-out: Per-app setting controlling whether your chats improve the model. Usually on by default.
Transformer: Architecture using self-attention so every token attends to every other token.
Triple: A subject-predicate-object fact such as (Paris, capitalOf, France).
TTFT (Time to First Token): How long until the model starts outputting; key latency metric for streaming.
Turing test: If a human cannot reliably tell a machine from a person in text chat, it can be said to think.

U

Uncensored model: An open-weight model whose safety and refusal training was removed or never added, so it rarely declines a request.
Unified memory: Apple Silicon's shared memory used to hold the model.
Usage cap / limit: A ceiling on messages or heavy requests in a period before you wait or upgrade.

V

Validation set: Data used to tune hyperparameters and compare models. Also called dev set.
Vector database: Storage that returns the stored vectors most similar to a query vector.
Vector space: A high-dimensional space where similar items land close together.
Verify, do not forward: AI output is a draft to check, not a fact to pass on.
Verify checkable claims: Confirm specific AI claims against an outside source.
Vibe coding: Describing what you want and letting the AI write the code, accepting it without reading or fully understanding it.
Vision-Language-Action model: A model mapping perception and language to robot actions.
Vision-language model: A model that reasons across text and images together (VLM).
Vocabulary: The fixed set of tokens, typically 32,000 to 200,000 entries.
Voice assistant: Software you talk to on a speaker, phone, or display.
Voice cloning: Using AI to copy a person's voice from a small audio sample, as little as three seconds.
Voice cloning and AI dubbing: Recreating a voice from a sample to read new lines and translating speech while keeping delivery.
VRAM: Graphics-card memory that must hold the whole model to run it.

W

Weights and biases: The numbers a network learns, where all its knowledge lives.
Word embeddings: The predecessor of the contextual embeddings transformers produce.
Workflow: A system where LLMs and tools are orchestrated through predefined code paths set in advance.
World model: A learned internal model of an environment an agent can simulate.

Z

Zero-shot: Prompting with no examples included.
Zero-shot CoT: Appending a trigger phrase like "Let's think step by step" to get reasoning with no examples.
Zero Data Retention: Business API mode where inputs and outputs are never logged, stored, or seen by reviewers.

#

"Nudify" deepfake: A tool that fabricates a fake nude from a clothed photo; against a minor it is CSAM under US law.