---
title: "prompt and context engineering | Skills We Assign For | Azendo"
description: "Prompt and context engineering — structure, examples, context budget, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/prompt-and-context-engineering/"
---

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

# prompt and context engineering.

Prompt and context engineering is the design of what a model receives: instructions, examples, retrieved material, tool definitions and output format. It is the primary lever on quality before fine-tuning is considered.

## Where prompt and context engineering fits on a long engagement.

Context selection matters more than prompt wording, and teams spend their effort the other way round. Which documents are retrieved, how much history is included and what is omitted determines what the model can possibly get right; phrasing adjusts how it uses what it has.

Long contexts are not uniformly attended to. Material in the middle of a large context is used less reliably than material at the start or end, so putting critical instructions and the most relevant retrieved passages at the boundaries is a real technique rather than superstition.

## What an assigned team does with prompt and context engineering.

Prompts are production code and belong under the same discipline. Version control, review, and evaluation before deployment — a prompt edited directly in a console is an untracked production change with no rollback.

Holding that standard is what separates a system that improves from one that drifts, and it is part of what is agreed as a [committed monthly capacity](https://azendo.co/pricing/) for AI work.

## What we use prompt and context engineering for.

* Context chosen deliberately What is retrieved and included decided by measurement rather than by filling the window.
* Critical material placed at the boundaries Instructions and key passages positioned where attention is most reliable.
* Prompts under version control Changes reviewed and evaluated rather than edited live in a console.

## How prompt and context engineering capacity is assigned.

Prompt engineering is assigned inside AI capacity, treated as production code rather than configuration.

## Roles we assign prompt and context engineering for

* [LLM Engineer AI engineering](https://azendo.co/services/ai-engineering/llm-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/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)

## Tell us what your roadmap needs prompt and context engineering 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/)