Course 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.
Hands-On Learning
Build an enterprise RAG pipeline using embeddings, hybrid search, and reranking workflows.
Mentor-Led Sessions
Mentor-led architecture reviews and implementation guidance using real enterprise knowledge scenarios.
Career-Ready Skills
Design production-grade RAG systems for enterprise AI applications.
Learning Outcomes
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
Prerequisites
Basic programming knowledge (Python recommended)
Familiarity with APIs and backend application development
Basic understanding of generative AI or machine learning concepts
Detailed Syllabus
Organized by professional domains with comprehensive coverage
Topics Covered:
- •Overview of retrieval-augmented generation architecture
- •Why RAG improves generative AI reliability
- •Enterprise knowledge use cases
- •Limitations of standalone LLM responses
Skills You'll Gain
Master these in-demand skills through hands-on practice
Career Progression
A clear view of the roles this programme supports, what typically comes next, and where learners progress over time
Ways to Learn
Choose the learning format that works best for you and your team
Live Online
Instructor-Led Training
Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- Live instructor interaction (real-time)
- Trainer-led walkthroughs and real examples
- Guided resources and session notes provided
- Structured Q&A and practical discussion
Price per person
Group enrolments and early planning options available.
All prices are exclusive of VAT where applicable. Group enrolments and custom packages available on request.
Prefer a Faster, Personalised Route into IT?
Not everyone learns best in a group. If you want focused guidance, faster clarity, and confidence you can use on the job, our 1-to-1 Fast-Track Training gives you private, mentor-led support tailored to your experience and goals.
"Many learners choose 1-to-1 when they want understanding, not memorisation."
Exam & Certification Information
Everything you need to know about the certification exams
Important Information
You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations.
Credential
Certificate of Completion
On successful completion of RAG Engineer, learners receive an Xcademia Certificate of Completion. This standalone certificate is issued directly by Xcademia and is aligned with globally recognised frameworks and best practices.
Frequently Asked Questions
Everything you need to know about this course
RAG is an AI architecture that combines document retrieval with generative models to produce responses grounded in real knowledge sources.
Ready to Start Your Learning Journey?
Take the next step in your professional development