Glossary
AI terms · in plain English.
What buyers actually ask about — RAG, agents, evals, MCP. Click any term for the deeper explanation.
A
- Agent
- An AI system that runs a workflow — fetches data, calls tools, decides between branches, escalates to humans when its confidence is low. Different from a chatbot (which answers questions) in that an agent takes actions.
- Agentic workflow
- A multi-step process where one or more AI agents coordinate to achieve an outcome. Often involves planning, tool use, retrieval, and human-in-loop checkpoints.
Related: Tool use · Agentic workflow · Guardrails
C
- Cache (prompt caching)
- Reusing the encoded representation of a long, static prompt prefix across many requests. Cuts cost 50-90% on use cases with large shared context (schemas, knowledge bases, instructions).
- Citations / grounding
- Requiring the AI to cite the source of each claim it makes from a retrieved document. The single most effective tool against hallucination in RAG systems.
- Context window
- How much text a model can consider at once. Claude has 200K+ token context windows; useful for working with large documents, codebases, or chat histories.
Related: RAG · Hallucination
E
- Embedding
- A dense numerical representation of text (or other media) that captures meaning. Used for semantic search, clustering, recommendation. Underlies most RAG systems.
- Eval / evaluation suite
- A set of test cases run weekly against an AI system to catch quality regressions before users do. Non-optional for production. We build one for every shipped system.
Related: RAG · Vector database
Related: Drift · LLM-as-judge
F
- Fine-tuning
- Continuing to train a model on your own data to bake in style, vocabulary, or task-specific behavior. Less needed than people think — prompting + RAG usually wins, except for brand voice and visual classification.
- Function calling / tool use
- When a model decides to call a defined function (search, lookup, write to DB) and the surrounding framework actually executes it. The core mechanism behind agents.
Related: Agent · MCP
H
- Hallucination
- When a model generates plausible-sounding but false content. Mitigated by RAG with citations, refusal patterns, retrieval-confidence thresholds, and eval coverage. Not zero, never zero.
- Hybrid search
- Combining keyword search (BM25) and semantic vector search and then reranking the results. Beats either alone on real-world queries. The default for production RAG.
Related: RAG · Reranking
L
- LLM (Large Language Model)
- The big text-prediction models that power everything — Claude, GPT, Llama, etc. Plural is plural, not capitalized LLMs.
- LLM-as-judge
- Using a strong model to evaluate the outputs of another model against your criteria. Used for eval suites at scale when human grading isn't tractable.
Related: Eval
M
- MCP (Model Context Protocol)
- Anthropic's open standard for connecting AI models to tools and data sources. Lets a model call a tool defined in any MCP server. We use it for ERP and internal-systems agents.
- Multi-agent system
- Multiple specialized agents coordinating to achieve a goal. Mirror-the-org-chart pattern works well — one agent per role you'd hire a human for. See the Multi-Agent Manufacturing case study.
Related: Tool use
Related: Agent
P
- Prompt
- The instructions + context you send to a model. Includes a system prompt (fixed, role-defining) and user/assistant messages (the conversation).
- Prompt engineering
- Crafting prompts to get reliable, high-quality outputs. Real skill. Includes few-shot examples, chain-of-thought, structured output, refusal patterns.
R
- RAG (Retrieval-Augmented Generation)
- Fetching relevant documents from a database first, then asking the model to answer using only those documents. The default architecture for grounded chatbots and knowledge assistants.
- Reranking
- Running retrieved candidates through a second model to reorder by relevance. Top-50 from vector search reranked to top-5 dramatically improves precision. We use Cohere Rerank.
Related: Embedding · Vector database · Reranking
S
- SCCs (Standard Contractual Clauses)
- EU-approved contract language for transferring personal data outside the EU. Included in our DPA. Required for most EU↔India data flows.
- Sandbox
- An isolated execution environment for untrusted code or agent actions. Used in our Auto Issue Resolution system to let Claude Code write fixes safely.
- Streaming
- Returning the model's response token-by-token as it's generated, instead of waiting for the full reply. Critical for chat UX — feels responsive even when the full response takes seconds.
- System prompt
- Fixed instructions at the top of every conversation — role, tone, allowed behaviors, refusal patterns. The lever you tune most often when shipping production AI.
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