October 2026 · Weekly batch
03 Oct 2026
09:00–15:00 · flexible hours
$799
5 of 25 seats available
Generative AI
Retrieval-Augmented Generation (RAG) Fundamentals in Birmingham, United Kingdom
Retrieval-Augmented Generation (RAG) Fundamentals is a practical course designed to build foundational skills in RAG, Generative AI, Large Language Models, embeddings, vector databases, and semantic search. Available in Birmingham, United Kingdom, this training covers document processing, chunking, retrieval pipelines, LLM integration, and RAG evaluation. Learners gain hands-on knowledge to understand and develop context-aware AI applications using modern RAG techniques
5 daysFoundation3.8 · 2,800 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 5 days.
October 2026 · Weekly batch
10 Oct 2026
09:00–15:00 · flexible hours
$799
10 of 25 seats available
October 2026 · Weekly batch
17 Oct 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
October 2026 · Weekly batch
24 Oct 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
October 2026 · Weekly batch
31 Oct 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
07 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
14 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
21 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
28 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
05 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
12 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
19 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
26 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
02 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
09 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
16 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
23 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
30 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
Course curriculum
What you’ll learn
Module 1: Introduction to Retrieval-Augmented Generation
- Understanding Retrieval-Augmented Generation (RAG) and its role in Generative AI.
- RAG architecture, components, workflow, and common applications in Birmingham, United Kingdom.
- Limitations of standalone Large Language Models and the need for external knowledge.
- RAG use cases across enterprise search, question answering, customer support, and knowledge management.
Module 2: Large Language Models for RAG
- Introduction to LLMs and their role in RAG applications.
- Understanding prompts, context windows, inference, and response generation.
- Connecting LLMs with retrieved information for context-aware responses in Birmingham, United Kingdom.
- Overview of popular LLM platforms and APIs used in RAG applications.
Module 3: Document Processing and Data Preparation
- Understanding documents and knowledge sources used in RAG systems.
- Document loading, cleaning, parsing, and preprocessing techniques.
- Handling PDF, text, web, and structured data sources.
- Preparing high-quality data for effective retrieval in Birmingham, United Kingdom.
Module 4: Text Chunking and Data Ingestion
- Understanding document chunking and its importance in RAG pipelines.
- Fixed-size, recursive, semantic, and metadata-based chunking approaches.
- Chunk size, overlap, and context considerations.
- Building efficient document ingestion workflows for RAG applications in Birmingham, United Kingdom.
Module 5: Embeddings and Semantic Search
- Understanding text embeddings and vector representations.
- How embedding models convert text into numerical representations.
- Semantic similarity and information retrieval concepts.
- Selecting and using embedding models for RAG applications in Birmingham, United Kingdom.
Module 6: Vector Databases and Retrieval
- Introduction to vector databases and their role in RAG systems.
- Storing, indexing, and searching vector embeddings.
- Understanding similarity search and metadata filtering.
- Overview of vector database technologies such as FAISS, Chroma, Pinecone, and Qdrant in Birmingham, United Kingdom.
Module 7: Building a Basic RAG Pipeline
- Designing the complete RAG workflow from ingestion to response generation.
- Connecting document loaders, chunking, embeddings, vector databases, and LLMs.
- Implementing retrieval and context injection techniques.
- Developing a basic RAG application through practical exercises in Birmingham, United Kingdom.
Module 8: Retrieval and Response Optimization
- Understanding retrieval quality and relevance.
- Improving search results through metadata, filtering, and retrieval strategies.
- Managing context length and reducing irrelevant information.
- Improving the quality and relevance of LLM-generated responses in Birmingham, United Kingdom.
Module 9: RAG Evaluation and Responsible AI
- Understanding key RAG evaluation concepts and performance factors.
- Evaluating retrieval accuracy, context relevance, and response quality.
- Identifying hallucinations, incorrect retrievals, and information gaps.
- Applying responsible AI, security, privacy, and data-quality considerations in Birmingham, United Kingdom.
Module 10: Practical RAG Project and Future Trends
- Building a practical RAG application using real-world knowledge sources.
- Implementing an end-to-end retrieval and generation workflow.
- Testing, troubleshooting, and improving the RAG application.
- Exploring advanced RAG, enterprise RAG, AI agents, and emerging RAG technologies in Birmingham, United Kingdom.
Entry requirements and prerequisites
- Basic Generative AI Knowledge – Familiarity with basic Generative AI concepts and Large Language Models is recommended for learners in BirminghamUnited Kingdom
- Basic Python Knowledge – Understanding Python fundamentals is beneficial for completing practical RAG exercises in BirminghamUnited Kingdom
- Basic AI and Machine Learning Awareness – A general understanding of AI, machine learning, and natural language processing concepts is helpful
- Basic Programming Concepts – Familiarity with variables, functions, data structures, and APIs can support hands-on learning
- No Prior RAG Experience Required – Learners do not need previous experience with RAG, embeddings, or vector databases to join the course in BirminghamUnited Kingdom
- Interest in Generative AI – An interest in LLMs, AI applications, semantic search, and modern AI technologies will help learners gain more from the training
Career value
Reasons to choose this course
Build Strong RAG Foundations
Understand the core concepts, architecture, and workflows of Retrieval-Augmented Generation in Birmingham, United Kingdom.
Learn Document Retrieval Techniques
Explore document ingestion, chunking, indexing, retrieval, and context preparation for RAG applications in Birmingham, United Kingdom.
Understand Embeddings and Vector Search
Learn how embeddings and similarity search support accurate information retrieval in RAG systems in Birmingham, United Kingdom.
Explore Vector Databases
Gain foundational knowledge of vector databases used to store and retrieve information for AI applications in Birmingham, United Kingdom.
Connect RAG with LLMs
Understand how retrieved information can be combined with Large Language Models to generate context-aware responses in Birmingham, United Kingdom.
Develop Practical RAG Skills
Apply RAG concepts through practical exercises, examples, and implementation-focused learning in Birmingham, United Kingdom.
Advantages
Turn learning into practical capability
Improve AI Knowledge Access
Understand how RAG applications can connect LLMs with external knowledge sources to provide information-rich responses in Birmingham, United Kingdom.
Strengthen AI Solution Understanding
Develop the ability to understand the components and workflow of practical RAG-based AI solutions in Birmingham, United Kingdom.
Support Enterprise AI Applications
Explore how RAG can be applied to organizational knowledge, internal documents, customer support, and information-driven workflows in Birmingham, United Kingdom.
Enhance Context-Aware AI Development
Learn how relevant external information can provide additional context for LLM-powered applications in Birmingham, United Kingdom.
Understand Knowledge-Based AI Workflows
Gain insights into how documents, knowledge sources, retrieval processes, and language models work together in modern AI systems in Birmingham, United Kingdom.
Create a Pathway to AI Specialization
Establish foundational knowledge that can support further learning in enterprise RAG, LLMOps, AI agents, and Generative AI development in Birmingham, United Kingdom.
Course includes
Review the instruction, learning resources and learner support provided with this programme. The course-specific details below explain exactly what is included in your training experience.
- Comprehensive RAG Curriculum – Structured learning covering RAG architecture, retrieval workflows, embeddings, vector databases, LLM integration, and evaluation in Birmingham, United Kingdom.
- Live Instructor-Led Training – Interactive sessions with guided instruction, demonstrations, and practical explanations for RAG concepts in Birmingham, United Kingdom.
- RAG Architecture and Workflow – Learn the key components and end-to-end workflow of Retrieval-Augmented Generation systems.
- Document Processing and Chunking – Explore document ingestion, preprocessing, chunking strategies, and knowledge preparation for RAG applications.
- Embeddings and Semantic Search – Understand embeddings, vector representations, similarity search, and semantic retrieval techniques.
- Vector Database Learning – Gain foundational knowledge of vector databases, indexing, metadata filtering, and efficient information retrieval.
- LLM Integration with RAG – Learn how Large Language Models can use retrieved information to generate context-aware responses.
- Hands-On RAG Exercises – Practice building and working with RAG pipelines through implementation-focused exercises in Birmingham, United Kingdom.
- RAG Optimization Techniques – Explore methods for improving retrieval relevance, context quality, response generation, and overall RAG performance.
- RAG Evaluation and Responsible AI – Learn foundational approaches to evaluating RAG systems while considering accuracy, reliability, privacy, security, and responsible AI.
- Practical RAG Project – Apply course concepts by developing an end-to-end RAG application using real-world knowledge sources.
- StepMerit Course Completion Certificate – Receive a StepMerit Course Completion Certificate after successfully completing the RAG Fundamentals training requirements in Birmingham, United Kingdom.
Exam details
Understand the applicable assessment approach, preparation support and exam-readiness guidance before planning your certification attempt.
Exam Level: Beginner–Intermediate – Retrieval-Augmented Generation (RAG) Fundamentals
Exam Type: Online computer-based examination consisting of multiple-choice questions and practical RAG-based tasks in Birmingham, United Kingdom.
Number of Questions: 40 questions
Exam Duration: 90 minutes
Passing Score: 70% (28 out of 40 questions)
Question Format: Multiple-choice questions covering RAG architecture, document processing, chunking, embeddings, vector databases, semantic search, LLM integration, retrieval, and RAG evaluation.
Practical Assessment: Practical tasks may include document ingestion, chunking, embedding concepts, vector search, retrieval workflows, context integration, and analysis of RAG responses in Birmingham, United Kingdom.
Key Exam Topics: RAG fundamentals, LLMs, document processing, data ingestion, chunking strategies, embeddings, vector databases, semantic retrieval, RAG pipelines, response generation, evaluation, optimization, and responsible AI.
Prerequisites: Basic understanding of Generative AI and LLM concepts is recommended. Basic Python knowledge is beneficial but not mandatory.
Assessment Platform: StepMerit online assessment platform.
Certification: Successful candidates receive a StepMerit Course Completion Certificate.
Exam Preparation: Learners can prepare through instructor-led sessions, hands-on RAG exercises, practical demonstrations, assignments, and an end-to-end RAG project in Birmingham, United Kingdom.
Course conclusion
See how the programme brings the learning together through review, practical application and clear next steps for using the skills after training.
The Retrieval-Augmented Generation (RAG) Fundamentals course provides a strong foundation in modern RAG and Generative AI technologies. Learners gain practical knowledge of document processing, chunking, embeddings, vector databases, semantic search, retrieval workflows, LLM integration, and RAG evaluation. Through hands-on exercises and real-world use cases, participants learn how RAG systems can connect external knowledge with Large Language Models to generate relevant and context-aware responses.
This course is suitable for developers, AI professionals, data professionals, IT specialists, students, and AI enthusiasts seeking RAG training in Birmingham, United Kingdom. The knowledge gained can support further learning in Advanced RAG, enterprise AI, LLM applications, AI agents, and Generative AI development.
Enroll in RAG Fundamentals training in Birmingham, United Kingdom to build practical RAG skills and develop a strong foundation for modern AI application development.
Who should attend
Target audience for Retrieval-Augmented Generation (RAG) Fundamentals
- AI and Generative AI Professionals – Professionals seeking to strengthen their understanding of RAG architecture, LLM integration, and knowledge-based AI applications in Birmingham, United Kingdom.
- Software Developers – Developers interested in building AI applications using document retrieval, embeddings, vector databases, and Large Language Models in Birmingham, United Kingdom.
- Machine Learning Engineers – ML professionals looking to expand their skills in RAG pipelines, semantic search, retrieval workflows, and Generative AI solutions in Birmingham, United Kingdom.
- Data Scientists and Data Professionals – Data professionals interested in document processing, embeddings, vector search, and AI-powered information retrieval in Birmingham, United Kingdom.
- NLP Professionals – NLP practitioners seeking practical knowledge of semantic search, language models, text processing, and retrieval-augmented applications in Birmingham, United Kingdom.
- IT and Technology Professionals – IT professionals who want to understand modern RAG technologies and their applications across enterprise AI environments in Birmingham, United Kingdom.
- AI Solutions Consultants – Consultants who want to understand RAG architectures and identify suitable Generative AI use cases for organizations in Birmingham, United Kingdom.
- Business Analysts and Product Professionals – Professionals interested in identifying business applications for RAG-powered search, knowledge management, and intelligent automation in Birmingham, United Kingdom.
- Students and Graduates – Students and graduates seeking foundational knowledge of RAG, LLMs, vector databases, and Generative AI for technology careers in Birmingham, United Kingdom.
- Knowledge Management Professionals – Professionals looking to explore AI-powered approaches for enterprise knowledge retrieval and document-based question answering in Birmingham, United Kingdom.
- AI Enthusiasts and Career Switchers – Learners transitioning into AI who want to develop practical RAG and Generative AI knowledge in Birmingham, United Kingdom.
- Technology Leaders and Entrepreneurs – Business and technology professionals seeking to understand how RAG can support enterprise AI applications and knowledge-driven solutions in Birmingham, United Kingdom.
Job roles
Explore the professional roles where the knowledge and practical capabilities developed in this course can be applied.
- RAG Developer – Apply foundational knowledge of retrieval pipelines, embeddings, vector databases, and LLM integration to support RAG application development in Birmingham, United Kingdom.
- Generative AI Developer – Support the development of AI applications using RAG, LLMs, document retrieval, and context-aware generation techniques in Birmingham, United Kingdom.
- AI/ML Engineer – Use RAG fundamentals to contribute to AI and machine learning projects involving knowledge retrieval, semantic search, and LLM-based applications in Birmingham, United Kingdom.
- LLM Application Developer – Build or support LLM-powered applications that combine external knowledge sources with language model capabilities in Birmingham, United Kingdom.
- AI Solutions Consultant – Help organizations identify and understand RAG-based solutions for enterprise search, knowledge management, and intelligent automation in Birmingham, United Kingdom.
- NLP Engineer – Apply knowledge of text processing, embeddings, semantic search, and language models to NLP and RAG projects in Birmingham, United Kingdom.
- AI Business Analyst – Analyze business requirements and identify opportunities for RAG-powered knowledge retrieval and Generative AI applications in Birmingham, United Kingdom.
- Data and AI Engineer – Support data ingestion, document processing, vector indexing, and retrieval workflows for RAG solutions in Birmingham, United Kingdom.
- Knowledge Management Specialist – Help organize and prepare enterprise knowledge sources for AI-powered retrieval and question-answering systems in Birmingham, United Kingdom.
- Generative AI Solutions Associate – Support implementation, testing, evaluation, and documentation of RAG and Generative AI solutions in Birmingham, United Kingdom.
Career and organisation benefits
Understand how this training can support individual career development while helping organisations strengthen capability, consistency and performance.
- Build In-Demand RAG Skills – Develop foundational expertise in Retrieval-Augmented Generation, LLM integration, embeddings, and semantic retrieval to support AI career development in Birmingham, United Kingdom.
- Expand Generative AI Knowledge – Strengthen your understanding of how RAG works with Large Language Models and modern AI applications in Birmingham, United Kingdom.
- Support AI Career Growth – Develop practical knowledge that can complement roles in AI development, NLP, data engineering, and Generative AI in Birmingham, United Kingdom.
- Develop Practical AI Project Skills – Gain hands-on experience with RAG workflows, document processing, vector search, and knowledge retrieval in Birmingham, United Kingdom.
- Prepare for Advanced AI Technologies – Build a foundation for further learning in Advanced RAG, AI agents, LLMOps, and enterprise AI applications in Birmingham, United Kingdom.
- Strengthen Technical Collaboration – Improve your ability to communicate with developers, data teams, AI engineers, and business stakeholders involved in RAG projects in Birmingham, United Kingdom.
Organization Benefits of Retrieval-Augmented Generation (RAG) Fundamentals
- Improve Knowledge Access – Help teams understand how RAG can connect organizational knowledge sources with AI applications for easier information access in Birmingham, United Kingdom.
- Support AI-Powered Search – Develop knowledge of retrieval technologies that can support intelligent search and knowledge discovery solutions in Birmingham, United Kingdom.
- Enhance Customer Support Workflows – Understand how RAG can support AI-powered question answering and context-aware customer service applications in Birmingham, United Kingdom.
- Strengthen Enterprise AI Adoption – Build employee knowledge that can support the evaluation and adoption of RAG-based Generative AI solutions in Birmingham, United Kingdom.
- Improve AI Solution Collaboration – Enable technical and business teams to better understand RAG architecture, workflows, and implementation requirements in Birmingham, United Kingdom.
- Support Knowledge Management Initiatives – Apply RAG concepts to enterprise documents and knowledge repositories to support modern AI-enabled knowledge management in Birmingham, United Kingdom.
Helpful answers for planning your training
Frequently asked questions
RAG is an AI approach that combines information retrieval with Large Language Models to generate context-aware responses
You will learn RAG architecture, document processing, chunking, embeddings, vector databases, retrieval, and LLM integration
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