HyperTeamsGlossary
How to use this Not a chapter to read front to back but a reference page. Terms you have not met yet are expected — look one up whenever another chapter stalls you.
IT basics
Start here if you have no technical background. The detail is in the IT basics part.
| Term | Meaning |
|---|---|
| Memory vs storage | what is open now vs what survives power off. Why "restart it" works → detail |
| Path | the one-line address saying which folder a file is in → detail |
| Terminal · CLI | the window where you instruct the computer in text → detail |
| PATH · command not found | the folder list commands are looked up in. Usually fixed by a new terminal window |
| Runtime · Node.js | the program that executes code. Node.js is the one for JavaScript → detail |
| IP · port | which machine, and which program on it → detail |
| localhost | this very machine. An address that never leaves it |
| Domain · DNS | the name, and the desk that turns a name into an address |
| Server · client | the side that answers and the side that asks. A role, not a class of machine → detail |
| Cloud · on-premises | computers you rent and computers inside your walls |
| HTTP request · response | the single round trip the web is made of → detail |
| Status codes | 401 key · 403 permission · 404 address · 429 rate · 500 server |
| API · endpoint | the counter built for programs, and one address on it → detail |
| SDK · library | that counter pre-wrapped per language |
| JSON · CSV | the notation programs exchange, and a table held as text → detail |
| Encoding (UTF-8) | the agreement turning characters into numbers. Mismatch it and text turns to garbage |
| Table · row · column · key | the shape a database holds data in → detail |
| SQL | how you ask a database. SELECT reads; UPDATE/DELETE are hard to undo |
| Authentication · authorisation | who you are, and what you may do → detail |
| API key · token | the key for programs. One per purpose; retire it if it leaks |
Basics
| Term | Meaning |
|---|---|
| Language model | a program that predicts the next word. Summarising, translating, and drafting all come from this → more |
| Prompt | everything you give the model: instruction + context + material |
| In-context learning | changing behaviour by putting examples in the prompt. One kind of learning, but the model does not change and it is gone when the conversation ends → more |
| Token | the unit of billing and length. Roughly one to two per English word |
| Hallucination | a plausible wrong answer. Structural, not a defect → more |
| Training cutoff | the end date of what the model learned. It knows nothing after it |
Conversation and memory
| Term | Meaning |
|---|---|
| Context window | everything the model sees at once. What "remembering" actually is → more |
| Context overflow | the window fills and the earliest part falls off → more |
| Context rot | quality sliding quietly as clutter accumulates, before anything overflows → more |
| Compaction | keeping recent turns verbatim and replacing earlier ones with a summary |
| Memory | a record written outside the conversation and re-read in later sessions → more |
Working with material
| Term | Meaning |
|---|---|
| RAG · grounding | finding relevant material and passing it in with the question. Non-parametric memory — kept outside the model, so the store learns rather than the model → more |
| Embedding | text turned into a list of numbers. Close in meaning, close in numbers |
| Semantic search | retrieval by closeness of meaning, using embeddings |
| Chunk | a piece a document is split into for retrieval |
| Rerank | a second pass that filters and orders retrieved chunks |
| Pre-training | the stage where a model learns language itself from a vast corpus. Most of its ability is settled here, and you do not do this part → more |
| Fine-tuning | further training, laying your data on top of an already pre-trained model. One part of training, and for voice and format rather than facts → more |
Tools and agents
| Term | Meaning |
|---|---|
| Tool use | the model calling an external capability. The system executes it → more |
| Agent | a structure that takes a goal and loops, choosing tools as it goes → more |
| MCP | the standard for attaching tools → more |
| MCP server | the side that exposes capabilities through that standard |
| Subagent · delegation | splitting large work and handing pieces to other agents |
| HITL (human in the loop) | placing human approval in front of irreversible actions → more |
| Guardrail | any mechanism that stops speed from turning into errors |
Models and cost
| Term | Meaning |
|---|---|
| Model tier | top, mid, light. Capability and price move together → more |
| Input/output tokens | what goes in and what comes out. Output costs roughly 5x |
| Caching | storing a repeated prefix and reusing it to cut cost |
| Thinking depth | how hard it reasons before answering. Higher is slower and pricier |
| Credit | the balance usage is drawn from in Connect → more |
Evaluation and operations
| Term | Meaning |
|---|---|
| Evals | a system for checking answer quality in numbers → more |
| Golden dataset | test cases with confirmed correct answers |
| LLM-as-judge | having a model do the scoring |
| Baseline | the measurement taken before deployment. Without it you cannot prove effect → more |
| Cycle time | time to process one item. The metric executives read most easily |
| Rework rate | share of AI output a human had to fix. A hidden cost |
| Trace | a tree-shaped record of the model and tool calls one request branched into → more |
Security and regulation
| Term | Meaning |
|---|---|
| Prompt injection | an attack where text in material the model reads acts as an instruction → more |
| Indirect injection | the form arriving via web, document, or email with no user action |
| Least privilege | grant only what is needed now, widen later → more |
| Irreversible action | sending email, payment, deletion, external publishing. The criterion for approval points |
| High-impact AI | AI in fields materially affecting rights — hiring, lending, healthcare → more |
| AI user-business | a business supplying products or services using someone else's AI. Most companies |
| Watermark / labelling duty | the obligation to mark output as AI-generated |
Transformation and method
| Term | Meaning |
|---|---|
| Adoption vs transformation | inserting a tool versus moving a metric → more |
| POC | the stage where you build it and confirm the effect → more |
| Gate | the criterion for whether you may proceed to the next stage |
| Hyper-agile | a short loop of intent → orchestration → validation → delivery → more |
| Micro sprint | that loop taken at its shortest → more |
| IntentOps | treating intent itself as something to be managed → more |
Product terms — HyperTeams
| Term | Meaning |
|---|---|
| HyperTeams | the control room for driving and watching your own machine from the web → more |
| Work folder | the folder scope an agent is allowed to touch → more |
| Autonomy level | how far it may go on its own → more |
| Skill | a repeated procedure captured as a document for reuse → more |
| Tunnel | the add-on that lets you reach your machine from outside |
Product terms — Connect
| Term | Meaning |
|---|---|
| Workspace | the isolation unit where data does not mix → more |
| Thread | a single conversation → more |
| Department | people grouped along the org chart → more |
| Conversation group | conversations grouped by topic, with a visibility setting |
| Drive | the file store that feeds answers → more |
| System | one company machine attached to the workspace → more |
| Channel | a route by which conversations arrive from an outside service → more |
| Queue | the list of scheduled and waiting work → more |
| Playground | where you test an agent before putting it into service → more |
This page is here to come back to. On to the main track → Adoption is not transformation