AI Glossary
108 AI and LLM-engineering terms, defined precisely — answer first, with the deeper guide linked.
A
- A2A (Agent2Agent Protocol)A2A is an open protocol that lets AI agents discover each other's capabilities and delegate tasks across vendors, complementing MCP's tool connections.
- Agent EngineeringAgent engineering is the discipline of building reliable AI agents — designing the tools, context, guardrails, evals, and recovery paths around the model.
- Agent HarnessAn agent harness is the system around the model that makes it an agent — the loop, tools, context management, permissions, and recovery machinery.
- Agent MemoryAgent memory is how an AI agent retains information beyond its context window — working state during a task and persistent knowledge across sessions.
- Agent SkillsAgent Skills are reusable procedures packaged as folders with a SKILL.md file — loaded by an AI agent on demand when a task matches, now an open standard.
- Agentic AIAgentic AI is the class of AI systems that act toward goals — planning, calling tools, and iterating on results — rather than only generating content.
- AGENTS.mdAGENTS.md is a versioned instruction file that tells coding agents how to work in a repository — including commands, conventions, boundaries, and checks.
- AI AgentAn AI agent is an LLM-driven system that pursues a goal in a loop — calling tools, observing results, iterating — instead of returning one answer.
- AI App BuilderAn AI app builder is a tool that generates a working application from a natural-language description, usually with hosting and a backend included.
- AI ConnectorsAI connectors are the integrations that let an assistant like Claude read and act in tools such as Google Drive, Slack, and Notion; in Claude, MCP servers.
- AI Data AnalystAn AI data analyst is a tool that takes a data question, writes and runs the code or SQL to answer it, and returns a chart or summary you still have to check.
- AI OverviewsAI Overviews are Google's AI-generated summaries at the top of Search results, with source links; AI Mode is the fuller conversational version.
- AI SDRAn AI SDR is software that runs sales development work such as prospecting, outreach, and follow-up, with autonomy that varies enormously between vendors.
- AI SlopAI slop is low-effort, mass-produced AI-generated content — fluent, generic, and unchecked — flooding feeds, search results, and codebases.
- AI Workflow AutomationAI workflow automation is connecting apps so tasks run on triggers, with an AI model handling the judgment steps a fixed rule cannot express.
- AI WrapperAn AI wrapper is a product whose core function is a prompt and interface around a third-party model API, such as Claude or GPT, rather than its own model.
- Answer Engine Optimization (AEO)Answer engine optimization (AEO) is structuring content so search engines and AI assistants can lift a direct answer from it for AI Overviews and chatbots.
- Attention MechanismAttention lets a model weigh how relevant every other token is to each token, building a context-aware representation as a weighted blend of their values.
B
- Batch InferenceBatch inference processes many LLM requests asynchronously instead of one-at-a-time interactively — typically at ~50% discount via provider batch APIs.
- Brand Voice (in AI prompting)Brand voice, in AI prompting, is the documented tone, vocabulary, and style rules a model is given so every draft sounds like one company, not the model.
C
- Chain-of-Thought (CoT)Chain-of-thought prompting has a model work through intermediate reasoning steps before answering — improving accuracy on multi-step problems.
- ChunkingChunking splits documents into retrievable pieces before embedding — the RAG design decision that quietly determines retrieval quality.
- Claude CoworkClaude Cowork is the agentic mode of Anthropic's Claude app that completes multi-step knowledge-work tasks in your files, connectors, and browser.
- Claude PluginsClaude Plugins are installable bundles of skills, slash commands, sub-agents, and MCP connectors that set up Claude Code or Claude Cowork for a role or team.
- Code Execution (Code Interpreter)Code execution lets an AI assistant write and run real code in a sandbox, so numbers, files, and charts come from a computation rather than an estimate.
- Computer UseComputer use is an AI agent operating software through its real interface — reading the screen, moving the cursor, clicking, and typing like a person would.
- Constitutional AIConstitutional AI trains models against written principles — the model critiques and revises its own outputs by them, reducing reliance on human labels.
- Context EngineeringContext engineering is the discipline of curating exactly what enters an LLM's context window so it has the right information and nothing else.
- Context WindowThe context window is the maximum text — measured in tokens — an LLM can consider at once: prompt, conversation, documents, and its own output combined.
- Conversation IntelligenceConversation intelligence is recording sales calls and meetings, transcribing them, and turning transcripts into searchable notes, actions, and CRM records.
- Conversational AnalyticsConversational analytics is asking questions of governed business data in plain language and getting a chart or number back, without opening a dashboard.
- Cosine SimilarityCosine similarity measures how alike two embeddings are by the angle between them — the standard relevance score behind semantic search and RAG retrieval.
D
- Design TokensDesign tokens are named values for a design system's decisions — color, spacing, type, radius — stored once and referenced everywhere instead of hardcoded.
- Design-to-CodeDesign-to-code is turning a design — a Figma file, a screenshot, or a generated mockup — into working front-end code, increasingly by an AI agent.
- Diffusion ModelA diffusion model generates an image by starting from random noise and removing it step by step, guided by a prompt, until a coherent picture remains.
- DistillationDistillation trains a smaller model to imitate a larger one — using its outputs as training data to get most of the capability at a fraction of the cost.
- DPO (Direct Preference Optimization)DPO aligns a model to preferences directly from chosen-vs-rejected pairs — no reward model, no RL loop — simpler and more stable than classic RLHF.
E
- EmbeddingAn embedding is a vector of numbers representing text's meaning, placed so similar texts land close together — the foundation of semantic search and RAG.
- Embedding DimensionEmbedding dimension is the length of an embedding vector — how many numbers represent each text — trading capacity against storage and search cost.
- Eval DatasetAn eval dataset is the curated set of test cases — inputs with expected outcomes — that an LLM application's quality is measured against.
- Extended ThinkingExtended thinking is the reasoning tokens a model generates before its final answer, trading latency and cost for higher accuracy on hard problems.
F
- Few-Shot PromptingFew-shot prompting includes worked examples in the prompt so the model learns the task's pattern from demonstrations instead of instructions alone.
- Fine-TuningFine-tuning continues training a pretrained model on your own examples, changing its weights to teach durable behavior, format, or domain style.
- Flash AttentionFlashAttention is an IO-aware, exact attention algorithm that runs standard attention far faster and with less memory by tiling on-chip.
- Frontier ModelA frontier model is one of the most capable AI models available — the leading edge from labs like Anthropic, OpenAI, and Google, defining the state of the art.
- Function Calling (Tool Calling)Function calling lets an LLM request structured invocations of your code: describe tools with schemas, the model emits typed calls, your app executes them.
G
- Generative Engine Optimization (GEO)Generative engine optimization (GEO) is making content more likely to be cited or summarized in AI answers from ChatGPT, Gemini, or Google AI Overviews.
- Generative UIGenerative UI is an interface produced by a model from a prompt — a whole screen, flow, or prototype generated rather than assembled by hand.
- GroundingGrounding ties a model's output to verifiable sources — retrieved documents, tool results, citations — instead of training-data memory.
- GuardrailsGuardrails are programmatic checks around an LLM — validating inputs and outputs in code — enforcing safety and format rules a prompt alone can't guarantee.
H
- HallucinationA hallucination is fluent, confident output that is factually wrong or fabricated — plausible text unsupported by any source, the signature LLM failure mode.
- Human-in-the-Loop (HITL)Human-in-the-loop design inserts human judgment at decisive points in an AI workflow — approving actions, resolving ambiguity, owning the irreversible steps.
- Hybrid SearchHybrid search runs keyword (BM25) and semantic (vector) retrieval together and merges the results — catching both exact terms and paraphrases.
I
J
K
- Knowledge CutoffA knowledge cutoff is the date a model's training data ends, so it has no built-in knowledge of any event, release, or fact that came after it.
- KV CacheThe KV cache stores each token's attention keys and values so an LLM doesn't recompute the whole context per new token — the memory that makes generation fast.
L
- Lead EnrichmentLead enrichment is filling the missing fields on a lead or account record, such as title, headcount, or tech stack, from data sources outside your own system.
- LLM-as-JudgeLLM-as-judge uses a language model to score AI outputs against a rubric — evaluating quality at scale where exact-match metrics fail and humans don't scale.
- LLMOpsLLMOps is the practices and tooling for running LLM apps in production: prompt versioning, evals, tracing, cost and latency monitoring, and guardrails.
- llms.txtllms.txt is a proposed Markdown file at a site's root that gives AI models a curated summary and link list, so they can find the right pages at inference time.
- LoRA (Low-Rank Adaptation)LoRA fine-tunes a model by training small low-rank adapter matrices instead of all weights — a fraction of the memory and cost, nearly full-tune quality.
M
- MCP (Model Context Protocol)MCP is the open standard for connecting AI models to external tools and data: write one server, and any MCP client — Claude Code, IDEs, agents — can use it.
- Mixture of Experts (MoE)MoE is a model architecture where a router activates only a few expert subnetworks per token — huge total capacity, a fraction of the compute per token.
- Model RoutingModel routing sends each request to the cheapest model that can handle it, escalating only hard cases to a stronger model — cutting cost and latency.
- Multimodal AIMultimodal AI processes more than one kind of input or output — text, images, audio, video — in a single model, like an LLM that reads screenshots or speaks.
N
- Needle in a HaystackNeedle in a haystack is a long-context eval that hides a fact in filler text and tests whether the model can retrieve it at varying depths and lengths.
- No-Code AINo-code AI is building software or automations that use AI models through visual tools and prompts, without writing or maintaining code.
O
P
- PerplexityPerplexity measures how well a language model predicts a text sample — the exponential of its average per-token negative log-likelihood. Lower is better.
- Prompt CachingPrompt caching reuses the computed state of a repeated prompt prefix across requests — dramatically cutting cost and time-to-first-token for stable context.
- Prompt EngineeringPrompt engineering is the practice of designing an LLM's inputs — instructions, context, examples, and format — to reliably get the output you want.
- Prompt InjectionPrompt injection is an attack where untrusted content carries instructions an LLM then follows — overriding its task, leaking data, or triggering tool calls.
- Prompt TemplateA prompt template is a parameterized prompt — fixed instructions with variable slots — turning prompts from strings into versioned, testable components.
Q
R
- RAG (Retrieval-Augmented Generation)RAG retrieves relevant documents from your own data and injects them into an LLM's prompt at query time, grounding answers in facts the model wasn't trained on.
- ReAct (Reasoning + Acting)ReAct is an agent loop that interleaves reasoning with tool actions — Thought, Action, Observation, repeat — so the model plans, calls a tool, and revises.
- Reasoning ModelA reasoning model is an LLM trained to think before answering — generating internal reasoning tokens it can spend adaptively on hard problems.
- Red-Teaming (AI)AI red-teaming is adversarial testing — attacking your model or agent with jailbreaks, injections, and misuse scenarios to find failures before users do.
- RerankingReranking is a second-pass scoring step: a cross-encoder model re-orders the top results from fast retrieval so the truly relevant few rise to the top.
- Revenue IntelligenceRevenue intelligence is deriving deal risk, forecast accuracy, and coaching signals from captured customer conversations rather than from CRM fields.
- RLHF (Reinforcement Learning from Human Feedback)RLHF trains a model against human preferences: people rank outputs, a reward model learns the ranking, and the LLM is optimized to produce preferred responses.
S
- Sales Engagement PlatformA sales engagement platform is the system reps work out of: it sequences emails, calls, and tasks across channels and logs every touch back into the CRM.
- Semantic CachingSemantic caching reuses LLM responses keyed by meaning rather than exact text, matching queries by embedding similarity to cut cost and latency.
- Semantic LayerA semantic layer defines business metrics, dimensions, and joins once, so every query and every AI agent computes the same number the same way.
- Semantic SearchSemantic search retrieves results by meaning rather than keyword overlap — embedding queries and documents in one vector space and matching by similarity.
- SLM (Small Language Model)A small language model is a compact LLM — roughly 1–15B parameters — that runs cheaply or locally, trading peak capability for speed and deployability.
- Speculative DecodingSpeculative decoding speeds up generation: a small draft model proposes tokens, the large model verifies them in one parallel pass — same output, fewer steps.
- Structured OutputStructured output makes an LLM return data in a guaranteed shape — JSON matching your schema — so code can consume model responses without parsing prose.
- SubagentA subagent is a specialist agent a primary agent delegates to — running in its own context window with its own prompt and tools, returning only a summary.
- Synthetic DataSynthetic data is training or eval data generated by a model rather than collected from the world — filling gaps, balancing classes, bootstrapping fine-tunes.
- System PromptThe system prompt is the standing instruction layer an LLM receives before user input — defining its role, rules, tools, and tone for the whole conversation.
T
- TemperatureTemperature controls how random an LLM's token choices are: low values make output focused and repeatable, high values make it varied and creative.
- Test-Time ComputeTest-time compute is spending more computation at inference — longer reasoning, sampling, or search — to improve answers without retraining the model.
- Text-to-ImageText-to-image is generating a picture from a written prompt, using a model trained to turn a description into pixels — or, in some tools, vectors.
- Text-to-SQLText-to-SQL is turning a plain-language question into a SQL query a database can run, using a model grounded in your schema, documentation, and past queries.
- Token (LLM)A token is the unit LLMs read and write — a word fragment of roughly 3–4 characters in English. Models are priced, limited, and measured in tokens, not words.
- Token StreamingToken streaming delivers model output incrementally as it's generated — via SSE or websockets — so users see text immediately instead of waiting.
- TokenizationTokenization splits text into tokens — the sub-word units a model reads and writes — and maps each to an integer ID the model processes.
- Top-k SamplingTop-k sampling restricts an LLM's next-token choice to the k most probable tokens before sampling; lower k is more deterministic, higher k more diverse.
- Top-p (Nucleus Sampling)Top-p sampling restricts an LLM's next-token choices to the smallest set whose probabilities sum to p — cutting the long tail of unlikely tokens adaptively.
- Tracing (LLM)LLM tracing records every step of a model-driven request — prompts, tool calls, retrievals, tokens, latency — so multi-step behavior is debuggable.
- TransformerThe neural-network architecture (Vaswani et al., 2017) that uses self-attention to process sequences in parallel — the basis of nearly all modern LLMs.
- Tree of ThoughtsTree of Thoughts is a prompting method that explores multiple reasoning branches as a search tree, evaluating and backtracking among them.
V
- Vector DatabaseA vector database stores embeddings and answers nearest-neighbor queries fast — the retrieval layer under RAG and semantic search, using ANN indexes like HNSW.
- Vibe CodingVibe coding is building software by describing intent in natural language and letting an AI agent write the code, judging results by behavior.
- VLM (Vision-Language Model)A VLM jointly understands images and text — reading documents, screenshots, charts, and photos and reasoning about them in language.