---
url: "https://xcademia.com/courses/rag-engineer"
title: RAG Engineer
description: "Learn how to build enterprise RAG systems including chunking, hybrid search, reranking, and grounding for reliable AI applications.

"
publishedAt: "2026-03-16T11:35:42.958247+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "AID-0017"
level: Professional
duration_days: "4"
track: "RAG & Vector Databases"
category: "AI, Data & Analytics"
credential_tier: tier1
price_gbp: "2199"
---

# RAG Engineer

> Design reliable retrieval-augmented generation systems that connect large language models to enterprise knowledge sources. This mentor-led programme uses practical scenarios to build grounded AI systems with hybrid search, reranking, and evaluation techniques.

## Overview

Retrieval-Augmented Generation (RAG) has become a core architecture for enterprise AI systems, allowing large language models to produce answers grounded in organisational knowledge. Properly implemented RAG systems improve accuracy, reduce hallucinations, and enable AI assistants to safely access large document collections.

This mentor-led programme focuses on the engineering practices required to design reliable RAG pipelines. Participants learn how to structure documents, implement chunking strategies, build hybrid search pipelines, apply reranking techniques, and evaluate the quality of grounded responses.

Through practical scenarios, learners design end-to-end enterprise knowledge assistants while addressing safety, evaluation, and reliability challenges. By the end of the programme, participants will be able to implement scalable RAG architectures that integrate securely with organisational data and support production-ready AI systems.

## Prerequisites

- Basic programming knowledge (Python recommended)
- Familiarity with APIs and backend application development
- Basic understanding of generative AI or machine learning concepts

## What you will learn

- Design and implement retrieval-augmented generation pipelines
- Build document ingestion and chunking pipelines
- Implement hybrid search and vector retrieval strategies
- Improve retrieval quality using reranking techniques
- Evaluate and benchmark RAG system performance
- Deploy reliable enterprise knowledge-grounded AI systems

## Skills you will gain

- Retrieval-augmented generation architecture
- Document ingestion and chunking strategies
- Vector embeddings and semantic search
- Hybrid search pipelines
- Retrieval reranking techniques
- RAG system evaluation and reliability engineering

## Career progression

- RAG Engineer
- Generative AI Engineer
- AI Application Engineer
- ML Engineer
- AI Platform Engineer
- Data Engineer

## Curriculum

1. **Module 1**
2. **Module 2**
3. **Module 3**
4. **Module 4**

## Exam & certification

You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations.

## Delivery options

- **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more.
- **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning.
- **Blended** — Combine online and in-person learning for maximum flexibility and impact.

## Frequently asked questions

**1. What is Retrieval-Augmented Generation (RAG)?**

RAG is an AI architecture that combines document retrieval with generative models to produce responses grounded in real knowledge sources.



**2. Is this course practical?**

Yes. Participants build a working RAG pipeline during hands-on exercises.



**3. What types of applications use RAG systems?**

Enterprise knowledge assistants, AI search platforms, document intelligence systems, and internal support assistants.



**4. Do I need machine learning experience?**

Basic familiarity with AI or machine learning concepts is recommended but not mandatory.



**5. What roles benefit most from this training?**

AI engineers, ML engineers, generative AI developers, and data engineers building AI-powered knowledge systems.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0017 |
| Duration | 4 days |
| Level | Professional |
| Track | RAG & Vector Databases |
| Category | AI, Data & Analytics |
| Credential tier | tier1 |
| Price (GBP) | £2199 |

---

## About this content

This Markdown course profile is the citation-grade twin of [RAG Engineer](https://xcademia.com/courses/rag-engineer). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite.

When citing or quoting, please attribute *Xcademia* and link back to the source URL above.

- Source: https://xcademia.com/courses/rag-engineer
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt