---
title: "embeddings | Skills We Assign For | Azendo"
description: "Embeddings for semantic search and retrieval — chunking, model choice and the Azendo roles assigned for AI engineering."
url: "https://azendo.co/skills/embeddings/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# embeddings.

An embedding represents text, images or other content as a vector of numbers, positioned so that similar items sit close together. Embeddings are what make semantic search and retrieval-augmented generation work.

## Where embeddings fits on a long engagement.

Almost every useful language-model feature over private data depends on retrieval, and retrieval depends on embeddings. If the right document is not retrieved, no amount of prompting recovers it — which is why retrieval quality, not model choice, is where most of the improvement in these systems is available.

The decisions that matter are unglamorous: how documents are split, what metadata travels with them, which model produced the vectors, and what happens when you change it. Re-embedding a corpus after a model change is a migration, and it is worth knowing that before you have ten million vectors.

## What an assigned team does with embeddings.

Retrieval quality degrades as a corpus grows, and it degrades quietly — the system keeps answering, just less accurately. Nobody files a ticket for an answer that is plausible and wrong, which is why this needs measuring on a cadence rather than at launch.

Changing the embedding model means re-embedding everything, so the decision compounds. That kind of forward cost is what a standing assignment is for, scoped under [managed AI services](https://azendo.co/services/ai-engineering/) once the feature is live.

## What we use embeddings for.

* Search that understands meaning Finding relevant documents when the user did not use the same words the document did.
* Grounding answers in your own content Retrieval over internal documentation so generated answers cite something real.
* Fixing poor retrieval Chunking and metadata reworked on a system that answers confidently and wrongly.

## How embeddings capacity is assigned.

Retrieval work is assigned under AI engineering services, and it is usually where an underperforming AI feature is actually failing. An Azendo [service delivery manager](https://azendo.co/how-it-works/) answers for what that capacity delivers.

## Roles we assign embeddings for

* [LLM Engineer AI engineering](https://azendo.co/services/ai-engineering/llm-engineer/)
* [NLP Engineer AI engineering](https://azendo.co/services/ai-engineering/nlp-engineer/)

## Service lines it sits in

* [AI engineering](https://azendo.co/services/ai-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in ai and machine learning

* [PyTorch — skill we assign for](https://azendo.co/skills/pytorch/)
* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [scikit-learn — skill we assign for](https://azendo.co/skills/scikit-learn/)
* [Pandas — skill we assign for](https://azendo.co/skills/pandas/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)
* [MLflow — skill we assign for](https://azendo.co/skills/mlflow/)

## Tell us what your roadmap needs embeddings for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)