Dictionary

A

Managed access to leading foundation models on AWS, with tooling for RAG, agents, and guardrails.

Amazon SageMaker

AWS platform for building, training, and running ML/GenAI with MLOps built in.

Azure OpenAI Service

Microsoft’s managed access to OpenAI models with enterprise security, compliance, and monitoring.

D

A unified place for analytics data, combining data lake flexibility with warehouse structure.

E

Goal‑oriented assistants that do ERP work; gathering data, updating records, and handling approvals.

F

Function calling

Letting an AI call approved tools and APIs to do work, not just answer.

G

Generative AI (GenAI)

AI that creates new content, such as text, images and code, based on what you ask for.

Google Vertex AI

Google Cloud’s platform for building, evaluating, and deploying AI with integrated data services.

L

Large Language Model (LLM)

A neural language model trained on large corpora to understand and generate natural language.

LLM evals (evaluation)

Testing your AI like software, so answers are trustworthy and changes don’t break things.

Running AI in production; safely, observably, and at a predictable cost.

M

Model Distillation

Transferring knowledge from a large model to a smaller one to improve speed and cost-efficiency.

N

NetSuite AI (embedded)

AI features built into NetSuite to speed up finance and operations work.

O

OCI AI Services

Oracle Cloud’s prebuilt AI services (vision, language, anomaly) for enterprise workloads.

S

Semantic search

Finding meaning, not just keywords, so results match intent even if words differ.

T

Breaking text into tokens that models read and generate; the unit behind context and cost.

V

Vector database

A database built for embeddings, storing and searching by meaning.