Retrieval-Augmented Generation (RAG) Fundamentals in Glasgow, United Kingdom

• Build RAG Fundamentals – Develop practical knowledge of Retrieval-Augmented Generation concepts, architecture, workflows, and applications in Glasgow, United Kingdom
• Learn Generative AI Technologies – Understand how RAG works with Large Language Models to create context-aware AI applications in Glasgow, United Kingdom
• Master Embeddings and Semantic Search – Learn how embeddings and semantic search improve information retrieval for AI solutions
• Understand Vector Databases – Gain foundational knowledge of vector databases, indexing, similarity search, and retrieval techniques
• Develop Practical RAG Skills – Apply document processing, chunking, retrieval, and LLM integration through hands-on learning
• Prepare for AI Career Opportunities – Build job-relevant RAG skills for roles such as RAG Developer, Generative AI Developer, AI/ML Engineer, and LLM Application Developer in Glasgow, United Kingdom
• Support Enterprise AI Applications – Understand how RAG can support enterprise search, knowledge management, customer support, and AI-powered applications
• Earn a Course Completion Certificate – Successfully complete the training and receive a StepMerit Course Completion Certificate

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 Glasgow, 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 Glasgow, 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 Glasgow, 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 Glasgow, 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 Glasgow, 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 Glasgow, 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 Glasgow, 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 Glasgow, 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 Glasgow, 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 Glasgow, United Kingdom.

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 Glasgow, 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 Glasgow, 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 Glasgow, United Kingdom.