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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.

B

BAA (Business Associate Agreement): A contract required under HIPAA when a vendor processes Protected Health Information (PHI) on behalf of a covered entity. We sign BAAs for healthcare engagements and route through enterprise model tiers that include BAAs.

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.

D

DPA (Data Processing Agreement): Contract governing how a vendor processes personal data on your behalf. Required under GDPR and India's DPDP Act. We have a template ready or we redline yours.

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.

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.

G

Guardrails: Constraints that limit what an agent can do — what files it can touch, what actions need human approval, what topics it refuses. Differentiates production AI from a demo.

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.

I

Inference: Running a model to get a response. As opposed to training the model. Most LLM cost in production is inference.

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.

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.

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.

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.

T

Token: The unit of text models process. Roughly 4 characters or ¾ of a word in English. Costs and rate limits are denominated in tokens.

V

Vector database: A database optimized for similarity search over embeddings. pgvector if you're on Postgres, Pinecone / Qdrant / Weaviate when you need more scale.

Term missing? Email sales@aliansoftware.net and we'll add it.

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