Weekend batches
October 2026
09:00–15:00 · flexible hours
$799per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI & RAG
Retrieval-Augmented Generation (RAG) Fundamentals in Lithuania
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 Lithuania, 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
40 Hours/5 daysFoundation3.8 · 1,000 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 40 Hours/5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$799per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$799per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$799per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Hours/5 daysFoundation
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 Lithuania.
- 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 Lithuania.
- 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 Lithuania.
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 Lithuania.
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 Lithuania.
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 Lithuania.
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 Lithuania.
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 Lithuania.
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 Lithuania.
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 Lithuania.
Entry requirements and prerequisites
- Basic Generative AI Knowledge – Familiarity with basic Generative AI concepts and Large Language Models is recommended for learners in LithuaniaLithuania
- Basic Python Knowledge – Understanding Python fundamentals is beneficial for completing practical RAG exercises in LithuaniaLithuania
- 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 LithuaniaLithuania
- 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 Lithuania.
Learn Document Retrieval Techniques
Explore document ingestion, chunking, indexing, retrieval, and context preparation for RAG applications in Lithuania.
Understand Embeddings and Vector Search
Learn how embeddings and similarity search support accurate information retrieval in RAG systems in Lithuania.
Explore Vector Databases
Gain foundational knowledge of vector databases used to store and retrieve information for AI applications in Lithuania.
Connect RAG with LLMs
Understand how retrieved information can be combined with Large Language Models to generate context-aware responses in Lithuania.
Develop Practical RAG Skills
Apply RAG concepts through practical exercises, examples, and implementation-focused learning in Lithuania.
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 Lithuania.
Strengthen AI Solution Understanding
Develop the ability to understand the components and workflow of practical RAG-based AI solutions in Lithuania.
Support Enterprise AI Applications
Explore how RAG can be applied to organizational knowledge, internal documents, customer support, and information-driven workflows in Lithuania.
Enhance Context-Aware AI Development
Learn how relevant external information can provide additional context for LLM-powered applications in Lithuania.
Understand Knowledge-Based AI Workflows
Gain insights into how documents, knowledge sources, retrieval processes, and language models work together in modern AI systems in Lithuania.
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 Lithuania.
Exam format & assessment
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 Lithuania.
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 Lithuania.
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 Lithuania.
Bring your learning together
Course conclusion
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 Lithuania. 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 Lithuania to build practical RAG skills and develop a strong foundation for modern AI application development.
Target audience
- AI and Generative AI Professionals
- Software Developers
- Machine Learning Engineers
- Data Scientists and Data Professionals
- NLP Professionals
- IT and Technology Professionals
- AI Solutions Consultants
- Business Analysts and Product Professionals
- Students and Graduates
- Knowledge Management Professionals
- AI Enthusiasts and Career Switchers
- Technology Leaders and Entrepreneurs
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Professional & Training Experience: 5+ Years
- Course Training Experience: 120+ Hours of training focused on Retrieval-Augmented Generation, embeddings, semantic search, vector databases, LLM integration, document processing, and AI-powered business applications.
- Candidates Trained: 180+ candidates, including AI professionals, developers, data professionals, IT professionals, software engineers, business professionals, and AI enthusiasts.
- Training Delivery Experience: Experienced in instructor-led online and classroom training with practical demonstrations, coding exercises, hands-on labs, assignments, case studies, and project-based learning.
- RAG Expertise: Practical experience with RAG architecture, document ingestion, preprocessing, chunking, embeddings, retrieval, context management, prompt integration, LLM response generation, and RAG evaluation.
- Embedding & Semantic Search Experience: Hands-on knowledge of text embeddings, vector representations, similarity search, semantic retrieval, metadata filtering, and retrieval optimization.
- Vector Database Experience: Practical exposure to vector database technologies and their integration into AI and RAG applications for efficient knowledge retrieval.
- LLM Integration: Experience integrating Large Language Models with retrieved business context to develop grounded, context-aware, and knowledge-based AI responses.
- Business Use Cases: Training and project experience applying RAG concepts to business knowledge management, customer support, document search, FAQs, product information, internal knowledge systems, and enterprise information retrieval.
- RAG Evaluation & Optimization: Experience evaluating retrieval quality and generated responses and improving chunking strategies, embedding selection, retrieval parameters, prompts, and context handling.
- Security & Responsible AI: Practical training experience covering data privacy, access control, sensitive information protection, hallucination management, response validation, and human-in-the-loop processes.
- Hands-On Project Guidance: Experience guiding learners through end-to-end RAG projects involving document processing, embeddings, vector search, LLM integration, retrieval workflows, testing, and response evaluation.
- Assessment Experience: Experienced in evaluating learners through quizzes, assignments, practical coding exercises, RAG implementation tasks, scenario-based activities, and project demonstrations.
Documented learner outcomes
- Problem Statement – A business handles large volumes of product information, company policies, service documents, FAQs, reports, manuals, customer-support resources, and internal knowledge. Employees may spend significant time searching through documents to find relevant information and prepare accurate responses. Traditional keyword-based search may not always understand the meaning behind user queries, while general-purpose AI models may generate inaccurate or unsupported responses when they lack access to current business information. The challenge is to develop a RAG-based AI solution that can retrieve relevant business knowledge and provide accurate, contextual, and reliable responses while maintaining data security and human oversight.
- How We Overcame the Problem – Learners analysed business information requirements and designed a RAG workflow for connecting organisational knowledge with an LLM. They collected and processed business documents, applied text cleaning and chunking techniques, generated embeddings, stored knowledge in a vector database, and implemented semantic retrieval. Learners then combined retrieved information with user queries and prompts to generate contextual responses, evaluated the results, and refined the retrieval and generation workflow to improve accuracy and relevance.
- Overall Implementation – Learners implemented an AI-powered business knowledge workflow covering business requirement → document collection → data preparation → text chunking → embedding generation → vector storage → user query → semantic retrieval → context integration → LLM processing → response evaluation → final validated response.
- Business Knowledge Ingestion – Learners prepared product documents, company policies, service information, FAQs, reports, manuals, and other approved business resources for ingestion into the RAG system.
- Document Processing and Chunking – Learners applied document preprocessing and chunking techniques to divide large business documents into meaningful information segments that could be efficiently retrieved.
- Embedding Generation – Learners converted processed business content into vector representations using embedding models, enabling the system to identify information based on semantic meaning rather than only matching keywords.
- Vector Database Integration – Learners stored embeddings and associated metadata in a vector database to support efficient similarity-based retrieval of relevant business information.
- Semantic Search and Retrieval – Learners developed retrieval workflows that accepted natural-language business questions and identified the most relevant document chunks based on semantic similarity.
- Context-Aware Response Generation – Learners combined retrieved business information with user queries and structured prompts before passing the context to an LLM, enabling the model to generate responses grounded in relevant organisational knowledge.
- Business Query Assistance – Learners implemented RAG workflows for answering questions related to products, services, policies, procedures, FAQs, and other approved business information.
- Knowledge-Based Content Generation – Learners used retrieved business context to generate summaries, explanations, reports, FAQs, customer-support responses, and other business content while maintaining alignment with available source information.
- Retrieval Evaluation and Refinement – Learners evaluated retrieved results and generated responses for relevance, accuracy, completeness, and contextual consistency. They refined chunking strategies, retrieval parameters, embeddings, prompts, and context selection to improve overall RAG performance.
- RAG Security and Responsible AI – Learners addressed risks such as unauthorised data access, sensitive business information exposure, irrelevant retrieval, hallucinations, misleading responses, and improper use of confidential organisational data.
- Human-in-the-Loop Validation – Learners implemented review steps in which business users verify important AI-generated responses and retrieved information before the outputs are used for customer communication or operational decision-making.
- Evidence of Learning – Learners produced document-ingestion workflows, processed document samples, chunking strategies, embedding examples, vector database configurations, semantic search results, RAG prompt templates, retrieved-context examples, generated responses, evaluation criteria, refined workflows, and implementation documentation as evidence of their practical RAG capabilities.
- Overall Learner Outcome – By completing the implementation, learners demonstrated the ability to analyse business knowledge requirements, prepare and process documents, apply chunking and embeddings, integrate vector databases, implement semantic retrieval, connect retrieved context with LLMs, evaluate RAG responses, optimize retrieval workflows, address security risks, and develop responsible AI-powered knowledge solutions for a realistic business scenario.
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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