Glossary

Accelerate is an open-source library from Hugging Face that makes PyTorch training code run on any hardware and in distributed setups with minimal changes — including mixed precision and FSDP and DeepSpeed support.

An adapter is a small, freshly added network module injected into a frozen language model — only these modules are trained, while the base model itself stays unchanged.

Agentic RAG is a RAG architecture in which an agent drives retrieval — it decomposes the question, searches iteratively, checks the hits, and decides itself whether a second or third pass is needed.

An AI agent combines a language model with tools and works toward a goal across multiple steps — typically in a loop of observing, planning and acting.

An AI content workflow is a structured process for creating content in which AI assists across several steps: briefing, draft, editing, fact-checking and publishing. The human steers, reviews and remains responsible for the result.

AI data analysis means evaluating data (CSV, Excel and more) via natural language: an AI system writes and runs code behind the scenes to clean, calculate and visualise files — e.g. ChatGPT's Advanced Data Analysis.

AI image generation creates images from text prompts using generative models. Tools such as Midjourney, DALL-E, Adobe Firefly and Stable Diffusion are used in marketing for concepts, visuals and image editing.

AI knowledge management makes scattered company knowledge searchable and usable with AI. Documents are turned into vectors so employees get answers from natural-language questions — usually via retrieval-augmented generation.

AI marketing is the use of artificial intelligence and machine learning to analyse marketing data, automate content and delivery, and optimise campaigns in real time. Its main areas are content, personalisation, analytics and generative engine optimization.

AI meeting assistants are software tools that automatically record, transcribe, summarize online meetings and derive action items from them. Well-known examples include Otter, Fireflies, Fathom and Microsoft Copilot in Teams.

AI presentations are slide decks created with the help of generative AI. From a short prompt or an outline, tools like Gamma or Beautiful.ai automatically generate structured slides with text, layout and matching images.

An AI research assistant is an AI system that, given a question, searches the web in multiple steps, reads and cross-checks sources, and synthesises a cited answer or report — e.g. ChatGPT Deep Research, Gemini Deep Research or Perplexity.

AI video generation creates moving images from text or image prompts. Diffusion-transformer models such as Google Veo, Kling, Runway or Sora iteratively denoise latent frames into coherent video, usually as clips of a few seconds.

AI-powered personalization automatically tailors content, offers and recommendations to individual users. Models analyse behaviour and profile data to deliver more relevant experiences in real time — consent-based and privacy-compliant.

Aider is an open-source AI pair programmer for the command line that edits code directly in the Git repository and commits every change automatically.

Computational method in transformer models that weights which parts of the input text are most relevant for predicting the next token — instead of treating every part as equally important.

Audio normalization is the adaptive linear scaling of an audio signal to a target level — the tool first measures a reference value (peak, RMS or LUFS) and from that calculates the required gain.

A safety layer in Claude Code: a small LLM evaluates every tool call that is not explicitly allowlisted, live, and blocks destructive or unauthorised actions before they execute.

AutoGen is an open-source framework from Microsoft for building multi-agent applications with LLMs. Several AI agents solve tasks together in structured conversations — for example group chats or asynchronous message passing.

AutoGPT is an open-source experiment released in 2023 that runs a language model such as GPT-4 in an autonomous loop — it breaks a goal into subtasks itself, calls tools and works through them with minimal human intervention.

Automatic Speech Recognition (ASR), also called Speech-to-Text (STT), is the automatic conversion of spoken language into written text by an acoustic model.

Asynchronous API mode that collects many requests and processes them at a significant discount — results are typically delivered within 24 hours.

Batch inference is the bundled processing of many prompts in one pass — locally on a GPU or as an asynchronous API job — trading real-time latency for throughput and cost.

An AI benchmark is a standardized task suite that makes language models comparable — same prompts, same scoring, transparent results for reasoning, knowledge, or coding.

Browser use refers to AI agents that drive a real web browser — navigating, clicking, filling forms, reading content — to complete web tasks autonomously. The browser serves as both the tool and the perception surface.

Catastrophic forgetting describes the effect that a neural network loses previously acquired knowledge — wholly or partially — when it is trained further on new data; a key problem when fine-tuning language models.

Chain-of-Thought (CoT) is a prompting technique that asks the model to spell out its reasoning in intermediate steps — boosting accuracy on multi-step tasks.

Chroma (ChromaDB) is an open-source vector database for storing and searching embeddings. It is widely used in RAG systems to retrieve relevant text passages via similarity search and feed them to language models.

Chunking is the splitting of longer texts into smaller, self-contained pieces — the first step of any RAG pipeline because embeddings and retrieval work at the chunk level.

Claude Code is Anthropic's official AI coding tool — available as a terminal CLI, VS Code extension, JetBrains plugin and web app, with a strong focus on tool use and configurable workflows.

Claude Mythos (April 2026) is Anthropic's most powerful model to date — a new tier above the Opus line, distributed primarily through the Glasswing program to cybersecurity defenders.

Claude Opus 4.5 is Anthropic's top-tier model from November 2025 — the direct predecessor of Opus 4.6, bringing "Infinite Chats" and substantially reduced token usage.

Computer Use is a capability of modern AI models that lets them operate a computer like a human — see the screen, move the mouse, use the keyboard — to perform tasks across arbitrary applications without an API.

Constitutional AI is a training method developed by Anthropic in which an AI model critiques and revises its own behaviour against an explicit list of principles (a "constitution") — rather than relying solely on human ratings.

Context engineering is the discipline of deliberately curating and maintaining everything inside the LLM context window — system prompt, tool definitions, RAG hits, memory, conversation history.

Context Precision is a RAG evaluation metric (0–1) measuring how well the retriever ranks relevant contexts above irrelevant ones — the focus is ranking quality, not raw hit count.

Context Recall is a RAG evaluation metric (0–1) measuring how completely the retriever supplied the facts needed for the gold answer — every missing fact lowers the score.

The context window is the maximum number of tokens a language model can process at once — input and output combined.

Contextual Retrieval is an Anthropic RAG technique that prepends an LLM-generated context sentence to every chunk before indexing — sharply raising retrieval quality.

Continue.dev is an open-source, model-agnostic AI coding extension for VS Code and JetBrains — configured via YAML, working with commercial as well as local models.

Continued pretraining is the further training of an already pretrained language model on large amounts of domain text — before any classic fine-tuning begins.

Conversational marketing is a dialogue-based form of customer engagement that responds to prospects in real time via chat, messaging and AI chatbots. The goal is to guide users along the customer journey through conversation rather than rigid forms.

Cosine similarity measures how similar two vectors are by the angle between them — the default metric in RAG retrieval for comparing query embeddings against the embeddings stored in a vector index.

CrewAI is an open-source framework for orchestrating multiple autonomous AI agents. It assigns agents roles, goals and tools and has them collaborate in teams ("crews") to solve complex tasks through coordinated, multi-step workflows.

Cursor is an AI-first code editor — a fork of Visual Studio Code with AI deeply wired into the editor itself.

Datasets is an open-source Python library from Hugging Face for loading, processing and sharing machine-learning datasets. It uses Apache Arrow and memory-mapping to handle even very large amounts of data efficiently.

Three related concepts for digital audio levels: dBFS is the scale with 0 dBFS as the hard ceiling, headroom is the safety margin below the maximum, clipping is the hard cut-off when a signal exceeds it.

Janus-Pro-7B is DeepSeek's open-source multimodal model from January 2025 — unifying image understanding and image generation in a single 7B architecture, MIT-licensed.

DeepSeek V4 (April 2026) is the fourth generation of the Chinese open-weight MoE model — released as Pro (1.6T parameters) and Flash (284B) under MIT license, with a 1M-token context window.

Diffusers is an open-source Python library by Hugging Face for using and training diffusion models for image, video and audio generation. It is built on PyTorch and is structured into pipelines, models and schedulers.

DPO is a fine-tuning method that aligns language models directly to human preference pairs — without a separate reward model and without reinforcement learning.

DSPy is an open-source Python framework from the Stanford NLP group that lets you program LLM applications instead of hand-crafting prompts. Tasks are described through declarative signatures and modules; optimizers automatically derive effective prompts and examples.

An embedding is a numeric representation (vector) of text, an image, or other data in which semantically similar content sits close together in space.

Ensemble means combining several deliberately varied LLM runs or models whose findings complement each other. Multi-model orchestration drives these runs via orchestrators with sub-agents, so the union of results is larger than any single run.

Evals are systematic tests for LLM applications — fixed test cases, automatic scoring of the responses, result as a score. The foundation for objectively comparing prompt or model changes.

Faithfulness is a RAG evaluation metric (0–1) measuring how many claims in a response can actually be derived from the retrieved contexts — a direct hallucination indicator.

Few-shot prompting is a technique that includes a handful of input/output examples in the prompt so the model picks up the desired format and style.

Continued training of a pretrained model on a smaller, specialized dataset to adapt style, format or domain knowledge in a targeted way.

Flowise is an open-source low-code tool for building LLM applications and AI agents visually via drag-and-drop. It is built on the LangChain ecosystem and connects models, data sources and tools as nodes.

Function calling is a language model's ability to produce a structured function invocation instead of a text reply — the technical foundation for tool use and AI agents.

Gemini 3 Pro (November 2025) was Google's third flagship model with a 1M-token context window and native multimodality — now superseded by Gemini 3.1 Pro.

Gemini 3.1 Pro (February 2026) is Google's current flagship — 1M-token context, 64K output tokens, doubled reasoning performance over Gemini 3 Pro.

Gemma 4 is Google DeepMind's open model family from April 2026 — four sizes (E2B to 31B), 256k context, multimodal, Apache 2.0 licensed.

GGUF (GPT-Generated Unified Format) is the standard file format for quantised LLMs on the llama.cpp engine — a single `.gguf` file holds weights, tokenizer and metadata together.

GitHub Copilot is GitHub's AI coding assistant — an extension for common editors offering tab autocomplete, chat and multi-file edits, with GPT and Claude models selectable.

GLM-4.6 is Zhipu AI's open-weight MoE model from September 2025 — 355B parameters (32B active), 200k context, MIT-licensed, with a focus on coding performance.

GLM-5.1 is Z.ai's open-source flagship from April 2026 — 744B parameters (40B active), 200k context, designed for agentic coding with up to 8 hours of autonomous runtime.

GPT-4o (May 2024) was OpenAI's first natively multimodal model — text, audio and image in a single model, with a real-time voice mode and substantially lower cost than GPT-4 Turbo.

GPT-4o mini (July 2024) is OpenAI's small, low-cost variant of GPT-4o — positioned as the successor to GPT-3.5 Turbo, clearly stronger at a much lower price per token.

GPT-5.2 is OpenAI's model generation from December 2025 — three variants (instant, thinking, Pro), a 400k context window and new state-of-the-art on professional knowledge work.

GPT-5.3 is an iteration of OpenAI's GPT-5 line — the Instant variant launched on March 3, 2026 with a focus on factual accuracy, shorter preambles and a more natural conversational flow.

GPT-5.3-Codex is OpenAI's specialized agentic coding model (February 2026), the successor to GPT-5.2-Codex — 25% faster, with substantial gains on Terminal-Bench and OSWorld.

GPT-5.3-Codex-Spark is OpenAI's coding model from February 2026 — a smaller, real-time-optimized variant of GPT-5.3-Codex delivering over 1000 tokens per second on Cerebras hardware.

GPT-5.4 is OpenAI's reasoning model from March 5, 2026 — the first mainline model with the coding capabilities of GPT-5.3-codex, native computer-use controls and 1M-token context.

GPT-5.5 is OpenAI's flagship from April 23, 2026 — positioned as its "smartest and most intuitive model" and pitched as a step toward AI that works across tools on its own.

GPT4All is an open-source desktop application by Nomic AI for running large language models locally and offline on your own computer. It works without a GPU and without a cloud connection, so data never leaves the device.

GraphRAG is a RAG variant that uses a knowledge graph in addition to vector search — answers come from linked entities and relationship paths, not just similar text.

Grok 4 is xAI's reasoning model from July 2025 — text and image input, 256k context, trained via reinforcement learning on the 200,000-GPU Colossus cluster.

Guardrails are protective layers around a language model that inspect inputs and outputs to catch unwanted behaviour — off-topic answers, PII leaks, unsafe actions.

A hallucination is a language-model response that sounds plausible but is factually wrong or fabricated — typically caused by statistical guessing without a factual anchor.

Haystack is an open-source AI orchestration framework from deepset for building production-ready LLM applications. Modular components — retrievers, generators, routers, tools — are assembled into explicit pipelines for RAG, agents and semantic search.

Hugging Face is the central platform of the open-source AI community — a hub for models, datasets, and demos, and the maintainer of widely used libraries like Transformers, Diffusers, and PEFT.

Central platform for openly shared AI models, datasets and demos — a kind of GitHub for machine-learning artifacts.

Hybrid search combines lexical full-text search (e.g. BM25) with semantic vector search and unites the strengths of both — precise on keywords and robust on meaning.

HyDE (Hypothetical Document Embeddings) is a RAG technique where an LLM first drafts a fake answer to a query — and then uses that answer's embedding to find real documents.

Indirect prompt injection is an attack that hides malicious instructions inside external content — web pages, documents, emails — that an LLM then processes and unintentionally executes.

Inference is the act of running an already-trained model to turn an input into a response — production use, not training.

Tokens you send to an AI model in an API call — your prompt, the context, attached documents. Billed separately from output tokens and usually much cheaper.

Instruction tuning is a variant of supervised fine-tuning in which a language model is trained on a broad mix of instruction-response pairs — so it reliably follows instructions across arbitrary tasks.

A jailbreak deliberately bypasses a language model's safety and behaviour rules through cleverly crafted prompts, making the model produce content it would normally refuse.

Jan is an open-source desktop application that lets you run large language models locally and fully offline on your own computer. It uses the llama.cpp engine and is positioned as a privacy-friendly ChatGPT alternative.

A keyword glossary for Speech-to-Text is a short, curated list of project-specific terms, brand and product names handed to an STT model as a context hint before transcription — typically through Whisper's `initial_prompt`.

Kimi K2 is Moonshot AI's open-weight MoE model series with 1T total parameters and 32B active parameters, trained on 15.5T tokens — focused on agentic and coding tasks.

Kimi K2.6 is Moonshot AI's open-weight agent model from April 2026 — 1T parameters (32B active), 262k context, natively multimodal, with agent swarms of up to 300 sub-agents.

Knowledge distillation is a training technique in which a small student model learns from the behaviour of a large teacher model — aiming for comparable quality at significantly lower resource cost.

The KV cache (key-value cache) stores the already computed key and value vectors of a language model's attention layers during text generation. This avoids recomputing the entire context for each new token and significantly speeds up inference.

Open-source framework that chains LLMs with data sources, tools and memory into applications — arguably the best-known pipeline library for AI apps.

LangGraph is an open-source framework from LangChain for stateful, graph-based LLM workflows — nodes are functions or agents, edges define the control flow including loops and branches.

Llama 3.2 is Meta's model generation from September 2024 — the first with vision support (11B/90B) and lightweight edge models (1B/3B) for mobile and on-device use.

Llama 4 Maverick (April 2025) is Meta's large Mixture-of-Experts variant in the Llama 4 lineup — 17B active out of 400B parameters across 128 experts, 1M-token context.

Llama 4 Scout (April 2025) is Meta's smaller Mixture-of-Experts variant in the Llama 4 lineup — 17B active out of 109B parameters, 10M-token context, multimodal.

Llama-3-SauerkrautLM-70b is a German-language DPO fine-tune of Meta Llama 3 70B, developed by VAGOsolutions and Hyperspace.ai.

llama.cpp is an open-source C++ library for efficient LLM inference on CPU and GPU — the engine many local tools like Ollama and LM Studio are built on.

Llamafile is a Mozilla project that packages a local language model as a single executable file — model weights and inference engine in one. That file runs on several operating systems without installation.

Data framework for LLM applications, specialized in indexing, enriching and retrieving private data — at the core of many RAG architectures.

LLM stands for Large Language Model — a neural network trained on large volumes of text to interpret and generate natural language.

LLM-as-a-Judge is the practice of using a language model as an evaluator — it compares responses from other models or scores outputs against given criteria.

LM Studio is a desktop application for running LLMs locally with a graphical interface — including a model browser, chat interface and OpenAI-compatible server.

Low-Rank Adaptation — parameter-efficient fine-tuning that trains only small additional matrices instead of all model weights.

Make (formerly Integromat) is a cloud-based no-code workflow automation platform. Through a visual interface, you connect apps and APIs into multi-step workflows ("scenarios") without programming — for example to pass data between services.

MCP — Model Context Protocol — is an open standard from Anthropic for connecting AI models to external data sources and tools through a unified interface.

Meta-prompting is the technique of having an LLM write or improve a prompt for a task rather than formulating it yourself — the prompt becomes the output.

Milvus is an open-source, distributed vector database for fast similarity search over large sets of embeddings. It is licensed under Apache 2.0 within the LF AI & Data Foundation; the main developer is the company Zilliz.

MiniMax is an AI lab founded in Shanghai in 2021 whose open-weight M model family (LLMs) targets very long context windows and low running costs via sparse attention.

Mistral Large 3 (December 2025) is Mistral's open-weight MoE flagship — 675B parameters, 256K-token context, natively multimodal, Apache 2.0.

Mistral Small 4 is Mistral AI's open-weight MoE model from March 2026 — merging reasoning, vision and coding into one model, 119B parameters (6.5B active), Apache 2.0.

A model card is a standardised datasheet for an AI model — documenting purpose, training data, performance, limitations and ethical considerations in one place.

Ollama is a tool for running language models locally — Llama, Mistral or Qwen — operated via a CLI, with an OpenAI-compatible HTTP server for applications.

Open WebUI is an open-source chat interface for local and remote LLMs — a ChatGPT-like browser UX that runs as a Docker container and talks to Ollama, OpenAI-compatible APIs and more.

Claude Opus 4.6 is Anthropic's flagship model from February 2026 — top tier of the Claude 4 family with a 1M-token context window, stronger coding and new effort controls.

Claude Opus 4.7 is Anthropic's top-tier model from April 2026 — successor to Opus 4.6 with high-resolution image processing, a new xhigh effort level and task budgets for long agentic runs.

ORPO is a training method that unifies supervised fine-tuning and preference optimisation in a single step — leaner than the classic SFT-plus-RLHF pipeline.

Tokens an AI model produces as its response. Billed separately and usually three to five times more expensive than input tokens because the model has to actively generate them.

Path dependency describes how, during LLM generation, each early token shapes everything that follows — the first verbalized finding steers the entire remaining analysis in one direction.

PEFT bundles methods that adapt a pretrained LLM by training only a small fraction of its weights — typically under 1%, instead of all billions of parameters.

A global setting in Claude Code that controls how tool calls are handled: Plan (planning only), Default (asks for confirmation), Accept Edits (files free), Bypass (allow everything).

pgvector is an open-source PostgreSQL extension for vector search — adding embedding columns, distance functions, and ANN indexes (HNSW, IVFFlat) directly to an existing Postgres database.

Pinecone is a managed vector database service for RAG and semantic-search workloads — it stores embeddings, answers nearest-neighbour queries, and scales without the user operating the index themselves.

Prefix tuning is a PEFT method that trains a sequence of learned "virtual tokens" prepended to the input rather than any model weights — the base model itself stays frozen.

Prompt caching is an API feature in which a provider stores recurring prompt prefixes — making subsequent requests cheaper and faster because the cached portion is not reprocessed.

Prompt engineering is the discipline of steering AI models through carefully crafted input prompts — closer to precise writing than to classic programming.

Prompt injection is an attack that hides instructions inside an LLM's input data to make the model ignore or subvert its original instructions.

Prompt leaking is an attack that tricks an LLM into revealing its hidden system prompt or other confidential context contents — a special case of prompt injection.

A prompt library is a central, organized collection of reusable and versioned prompts within a team. It provides proven inputs for AI models so they do not have to be reworded every time they are used.

A prompt template is a reusable prompt skeleton with placeholders that get filled with concrete values at runtime — the basis for reproducible LLM calls in applications.

Qdrant is an open-source vector database written in Rust — it stores embeddings for semantic search, RAG and recommendation systems and runs locally, on your own cluster or as a managed cloud.

Extension of LoRA that quantizes the base model to 4 bits — allowing even very large LLMs to be fine-tuned on a single GPU.

Quantization reduces the numeric precision of model weights — e.g. from 16-bit float to 4-bit integer — making language models small and fast enough for consumer hardware, with manageable quality loss.

Query expansion enriches the original query in a RAG pipeline with synonyms, reformulations or hypothetical answer texts in order to lift retrieval recall.

Qwen 3.5 (February 2026) is Alibaba's third-generation open model family — from 0.8B to 397B parameters, MoE flagship with 1M-token context, multimodal.

RAG (Retrieval-Augmented Generation) connects a language model to an external knowledge source — relevant passages are retrieved and passed to the model alongside the question.

RAGAS is an open-source framework for automated evaluation of RAG and agent pipelines — with built-in metrics like faithfulness, context precision, and answer relevancy.

Provider-enforced cap on requests or tokens per time window — it protects infrastructure and ensures fair usage across customers.

ReAct is a prompting pattern that has an LLM alternate between reasoning (thoughts) and action (tool calls) — the foundation behind many agent implementations.

Reasoning Effort is a control parameter on modern reasoning models that sets how much internal step-by-step thinking (thinking tokens) a model spends before answering — higher levels raise quality but also latency and cost.

Recall (hit rate, sensitivity) measures the share of all actually existing relevant cases that a system finds. Formula: relevant cases found divided by all relevant cases that truly exist. Requires a known ground truth.

Reranking reorders an already-retrieved list of hits with a more accurate model — typically a cross-encoder that scores query and each candidate together, instead of just comparing vector distances.

Reinforcement Learning from Human Feedback — training procedure that aligns models with helpful and safe behavior using human preference comparisons.

Role prompting assigns the LLM a concrete role or persona ("you are an experienced tax lawyer …") to steer style, vocabulary and depth of answers.

Safetensors is a file format developed by Hugging Face for storing model weights — faster, safer and more language-agnostic than the older PyTorch `.pt`/`.bin` format.

Sampling is the weighted drawing of the next token from a language model's probability distribution — governed by temperature, top-p and top-k. It produces the variability between two runs.

SauerkrautLM is a German-language LLM family from the German startup VAGOsolutions — fine-tunes based on Llama, Qwen, Mistral, and other open architectures.

Self-Consistency is a prompting technique where the same question is answered multiple times with chain-of-thought, and the most frequent answer is picked as the final result.

Self-Refine is a prompting technique in which a model critiques its own output and improves it across several iterations — without human feedback.

Semantic search retrieves content by meaning rather than exact word match — query and documents are compared as embeddings, so synonyms and paraphrases also hit.

SFT is the supervised post-training of a pretrained language model on a dataset of input-output pairs — the step that turns a base model into a usable assistant.

Hugging Face Spaces is a hosting platform for deploying and sharing interactive machine learning applications without your own server infrastructure. Apps become usable directly in the browser, for example via Gradio, Streamlit, Docker or static HTML.

Speech-to-Text refers to the automatic conversion of spoken language into text by an AI model. In an AI workflow, STT replaces the keyboard as the input channel — what matters is model size and domain vocabulary.

Spotlighting is a defense technique against prompt injection in which untrusted inputs are marked so the model treats them as data — not as instructions.

Stop sequences are strings that make an LLM end token generation as soon as they appear. They constrain the output deliberately — to prevent role switches, format markers or overly long answers.

Streaming means transmitting an LLM's response token by token in real time — the user sees the text word by word rather than only after the full generation completes.

Structured output is a language model's ability to deliver responses in a defined schema (typically JSON), reliable enough to be consumed directly by downstream code.

Synthetic data is artificially generated training or evaluation data — typically produced by a language model itself to extend datasets without needing real-world sources.

A system prompt is the instruction that shapes a language model's behavior across an entire conversation — in contrast to the user prompt, which varies per message.

Temperature is a sampling parameter that controls how deterministic or creative a language model's responses are — low values stay conservative, high values produce more variety.

TensorRT-LLM is an open-source library from NVIDIA that optimizes LLM inference on NVIDIA GPUs. It compiles models into highly optimized runtime engines and offers in-flight batching, a paged KV cache and quantization for high throughput at low latency.

Text-Generation-WebUI (oobabooga) is an open-source web interface for running LLMs locally. It bundles several inference backends, chat and completion modes and an OpenAI-compatible API under one common UI.

TGI (Text Generation Inference) is an open-source toolkit from Hugging Face for serving large language models efficiently. It provides a production-ready inference server with continuous batching, token streaming and optimised attention methods.

The thinking budget (token budget) is the maximum number of internal reasoning tokens a reasoning model may spend on "thinking" per request — controlling the trade-off between answer depth and cost or latency.

Tiered model line-up from a provider — small fast variants (Mini/Flash/Haiku) at a fraction of the price of the big frontier models. Also: volume tiers with quantity discounts.

A token is the smallest unit a language model works with internally — usually a sub-word fragment, occasionally a single character.

A tokenizer is the program that splits text into tokens before a language model can process it — it determines how many tokens a piece of text costs.

An invocation of a tool by an AI model during a conversation — such as reading a file, running a bash command, fetching from the web or calling an MCP tool. The foundation of agentic workflows.

Top-p (nucleus sampling) and top-k are sampling strategies for LLMs that decide which token candidates to draw from at each step — together with temperature they control the creativity of the output.

TPM (tokens per minute) and RPM (requests per minute) are the two common units in which AI API providers express their rate limits — TPM caps the token volume, RPM caps the number of requests per minute.

Open-source Python library from Hugging Face that gives unified access to thousands of pretrained models across text, vision and audio.

Tree of Thoughts (ToT) is a prompting technique in which an LLM explores multiple solution paths as a branching tree, scores them and follows only the promising branches — a generalisation of Chain-of-Thought.

A vector database stores embeddings as high-dimensional vectors and searches them by semantic similarity — the core infrastructure for RAG, semantic search, and recommendation systems.

vLLM is an open-source high-throughput inference server for LLMs — known for PagedAttention, continuous batching, and an OpenAI-compatible API endpoint, widely used in production self-hosting setups.

VRAM (Video RAM) is the dedicated memory on a GPU — the single most important hardware figure for running language models locally, because ideally the whole model and its context must fit inside it.

Weaviate is an open-source vector database written in Go — it stores objects together with their embeddings, combines semantic and keyword (BM25) search and offers built-in vectorization, RAG and multi-tenancy.

Whisper is OpenAI's open speech-to-text model from 2022 — a multilingual encoder-decoder transformer that turns audio into text and ships in several sizes under an MIT license.

The `initial_prompt` parameter lets you pass Whisper a keyword list or sample sentence before transcription. The model treats this text as context and recognises the included terms far more reliably.

whisper.cpp is an open-source C++ port of OpenAI's Whisper speech recognition — a compact native binary for local transcription on CPU, CUDA, Metal or Vulkan, with no Python dependency.

Windsurf is an AI IDE built on the VS Code base with a focus on agentic coding workflows — the Cascade agent plans and executes multi-step changes autonomously.

XML tags are named brackets like <context>...</context> used to clearly separate sections of a prompt. They structure the input, split instruction from data and make the model's output more predictable.

Zapier AI bundles the AI features of the automation platform Zapier — including Copilot for building workflows by language, autonomous agents, chatbots and an MCP server that exposes thousands of Zapier actions to external language models.

Zero-shot describes solving a task with a language model without providing any examples — purely via the task description in the prompt.