AI engineering glossary

Short, sourced definitions you can quote, each linked to the matching roles and services. Ryz Labs provides senior engineering teams and AI pods that work on your team to build AI and software. Founded in 2021, headquartered in Calabasas, California.

Teams and roles

Forward-deployed engineer

A forward-deployed engineer (FDE) is a senior engineer who builds and ships software or AI systems directly in a client's environment, working alongside the client's own team.

AI pod

An AI pod is a dedicated, cross-functional team of senior engineers that designs, builds and ships an AI system as one unit, working in the client's cloud and repositories.

Staff augmentation

Staff augmentation is a way of adding outside engineers to an existing team, where they work under the client's own managers, processes and priorities.

MLOps engineer

An MLOps engineer builds and runs the infrastructure that takes machine learning models from experiment to production, including training pipelines, deployment and monitoring.

LLM evaluation engineer

An LLM evaluation engineer designs the tests, datasets and metrics that measure whether a large language model application gives accurate, safe and useful results.

AI building blocks

Retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) is a technique where a system first retrieves relevant information from a knowledge source and then passes it to a language model to generate an answer.

AI agent

An AI agent is software that uses a language model to decide which steps to take and which tools to call in order to complete a task, rather than only producing a single reply.

Vector database

A vector database stores numerical representations of data, called embeddings, and quickly finds the items most similar to a query, which makes it useful for semantic search and RAG.

LLM fine-tuning

LLM fine-tuning is the process of further training a pretrained large language model on a curated set of examples so it performs a specific task, format or style more reliably.

Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard for connecting AI applications to external data sources and tools through a common interface.