Weekend batches
October 2026
09:00–15:00 · flexible hours
$800per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI & RAG
Advanced Retrieval-Augmented Generation (RAG) in Liechtenstein
Advanced Retrieval-Augmented Generation (RAG) training helps learners master advanced RAG architectures, retrieval optimization, reranking, hybrid search, query transformation, and contextual response generation. Available in Liechtenstein, this course explores advanced embeddings, vector databases, RAG evaluation, and techniques for improving AI response accuracy. Learners gain practical knowledge of building scalable, reliable, and context-aware Generative AI applications using Large Language Models and advanced information retrieval techniques
40 Hours/5 daysAdvanced4.3 · 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
$800per 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
$800per 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
$800per 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 daysAdvanced
Module 1: Advanced RAG Architecture
- Understanding advanced Retrieval-Augmented Generation concepts and enterprise RAG workflows in LiechtensteinLiechtenstein
- Advanced RAG architecture and core components
- Retrieval, ranking, context construction, and generation layers
- Designing modular and scalable RAG pipelines
- Limitations of basic RAG systems
- Retrieval gaps and irrelevant context
- Context-window and hallucination challenges
Module 2: Advanced Document Processing
- Advanced document ingestion and knowledge preparation in LiechtensteinLiechtenstein
- Processing complex data sources
- PDFs, web pages, structured data, and enterprise documents
- Metadata extraction and document enrichment
- Data preprocessing techniques
- Cleaning and normalization
- Duplicate detection and document quality management
Module 3: Advanced Chunking and Contextual Retrieval
- Advanced text chunking strategies for improved retrieval in LiechtensteinLiechtenstein
- Chunking approaches
- Semantic and recursive chunking
- Hierarchical and structure-aware chunking
- Contextual retrieval techniques
- Preserving document relationships
- Improving contextual understanding across chunks
Module 4: Embeddings and Advanced Vector Search
- Advanced embedding techniques for high-quality semantic retrieval in LiechtensteinLiechtenstein
- Embedding models
- Selecting embedding models for different use cases
- Understanding embedding dimensions and similarity
- Vector search optimization
- Similarity metrics
- Indexing strategies
- Metadata filtering
Module 5: Hybrid Search and Reranking
- Implementing advanced retrieval strategies for RAG applications in LiechtensteinLiechtenstein
- Hybrid search
- Combining keyword and semantic search
- Balancing lexical and vector-based retrieval
- Reranking techniques
- Cross-encoder and relevance-based reranking
- Improving retrieved context quality
Module 6: Query Optimization and Retrieval Strategies
- Optimizing user queries for improved information retrieval in LiechtensteinLiechtenstein
- Query transformation
- Query expansion and rewriting
- Multi-query retrieval
- Advanced retrieval methods
- Parent-child retrieval
- Metadata-aware retrieval
- Multi-step retrieval workflows
Module 7: Advanced RAG with LLMs
- Integrating advanced retrieval workflows with Large Language Models in LiechtensteinLiechtenstein
- Context management
- Context selection and compression
- Reducing irrelevant information
- LLM response generation
- Prompt design for retrieved context
- Grounded and context-aware response generation
Module 8: RAG Evaluation and Optimization
- Evaluating and improving advanced RAG system performance in LiechtensteinLiechtenstein
- Retrieval evaluation
- Context relevance
- Retrieval accuracy and recall
- Generation evaluation
- Response relevance
- Faithfulness and groundedness
- RAG optimization
- Identifying retrieval and generation bottlenecks
- Improving overall system performance
Module 9: Production and Enterprise RAG
- Understanding production considerations for enterprise RAG solutions in LiechtensteinLiechtenstein
- Scalable RAG architecture
- Performance and latency considerations
- Data and knowledge-source management
- Security and responsible AI
- Access control and data privacy
- Reducing sensitive-data exposure
- Responsible use of enterprise knowledge
Module 10: Advanced RAG Project and Future Trends
- Developing an advanced RAG application using an end-to-end workflow in LiechtensteinLiechtenstein
- Practical implementation
- Data ingestion and advanced chunking
- Embeddings and vector search
- Hybrid retrieval and reranking
- LLM integration and response generation
- Project evaluation and optimization
- Testing retrieval quality
- Evaluating generated responses
- Troubleshooting and improving the RAG pipeline
- Future of RAG
- Agentic RAG and multimodal RAG
- Enterprise AI and advanced LLM applications
- Emerging RAG architectures and technologies
Entry requirements and prerequisites
- RAG Fundamentals – Understanding of basic RAG architecture, retrieval workflows, and Generative AI concepts is recommended for learners in LiechtensteinLiechtenstein
- Generative AI and LLM Knowledge – Familiarity with Large Language Models, prompting, context windows, and LLM application concepts is beneficial
- Python Programming – Basic to intermediate Python knowledge is recommended for implementing and testing advanced RAG pipelines in LiechtensteinLiechtenstein
- Embeddings and Vector Databases – Basic knowledge of embeddings, vector databases, similarity search, and semantic retrieval will support advanced learning
- Document Processing – Familiarity with document ingestion, preprocessing, chunking, and metadata handling is helpful
- API Knowledge – Basic understanding of REST APIs and connecting AI models through APIs is beneficial for practical exercises in LiechtensteinLiechtenstein
- No Advanced RAG Experience Required – Previous advanced RAG implementation experience is not mandatory, but foundational RAG knowledge is strongly recommended
Career value
Reasons to choose this course
Master Advanced RAG Architecture
Learn advanced Retrieval-Augmented Generation architectures and workflows for modern AI applications in LiechtensteinLiechtenstein
Improve Retrieval Quality
Explore query optimization, reranking, hybrid search, and advanced retrieval techniques
Learn Advanced Vector Search
Develop practical knowledge of vector databases, indexing, filtering, and similarity search
Optimize LLM Responses
Learn how to improve context integration and response generation using advanced RAG techniques in LiechtensteinLiechtenstein
Build Production-Ready RAG Skills
Understand approaches for developing scalable and reliable RAG applications
Explore RAG Evaluation
Learn techniques for measuring retrieval accuracy, relevance, faithfulness, and response quality
Advantages
Turn learning into practical capability
Develop Enterprise AI Knowledge
Understand how advanced RAG architectures can support enterprise knowledge systems, internal information platforms, and intelligent business applications in LiechtensteinLiechtenstein
Strengthen AI System Design Skills
Learn how to approach the design of complex RAG solutions by considering data sources, retrieval components, LLMs, workflows, and application requirements
Support Better Knowledge Utilization
Discover how RAG can connect organizational information with AI applications, helping users access relevant knowledge more effectively in LiechtensteinLiechtenstein
Expand LLM Application Expertise
Build deeper knowledge of integrating retrieval-based information with LLM-powered applications for practical business and technology scenarios
Explore Modern RAG Use Cases
Gain exposure to advanced applications across enterprise search, knowledge assistants, document intelligence, research systems, and AI-powered support solutions
Create a Foundation for AI Specialization
Use advanced RAG knowledge as a pathway toward specialized learning in LLM engineering, AI agents, enterprise Generative AI, and production AI systems in LiechtensteinLiechtenstein
Exam format & assessment
- Exam Level: Intermediate–Advanced – Retrieval-Augmented Generation (RAG) in LiechtensteinLiechtenstein
- Exam Type: Online computer-based examination combining multiple-choice questions and practical RAG implementation tasks
- Number of Questions: 40 questions
- Duration: 90 minutes
- Passing Score: 70% (28 out of 40)
- Question Format: Questions covering advanced RAG architecture, document processing, contextual chunking, embeddings, vector databases, hybrid search, reranking, query optimization, LLM integration, and RAG evaluation
- Practical Assessment: Tasks may cover advanced document ingestion, retrieval strategies, vector search, query optimization, reranking, context management, and RAG pipeline evaluation in LiechtensteinLiechtenstein
- Key Topics: Advanced RAG architectures, semantic and hybrid retrieval, embeddings, vector databases, contextual retrieval, reranking, query transformation, LLM integration, evaluation, optimization, and enterprise RAG
- Prerequisites: Basic RAG knowledge, Generative AI concepts, LLM fundamentals, and basic to intermediate Python knowledge are recommended
- Assessment Platform: StepMerit online assessment platform
- Certification: Successful candidates receive a StepMerit Course Completion Certificate
- Exam Preparation: Instructor-led training, hands-on exercises, practical demonstrations, assignments, and an end-to-end advanced RAG project in LiechtensteinLiechtenstein
Bring your learning together
Course conclusion
The Advanced Retrieval-Augmented Generation (RAG) course provides learners with advanced knowledge and practical skills for designing, developing, evaluating, and optimizing modern RAG systems. The training covers advanced document processing, contextual chunking, embeddings, vector databases, hybrid search, reranking, query optimization, LLM integration, RAG evaluation, and enterprise AI applications in LiechtensteinLiechtenstein
Through hands-on exercises and an end-to-end RAG project, learners gain practical experience in building scalable and context-aware Generative AI solutions. The course also introduces emerging technologies such as agentic RAG, multimodal RAG, and advanced LLM applications
Advanced Retrieval-Augmented Generation (RAG) is suitable for developers, AI/ML professionals, data professionals, LLM engineers, and technology specialists looking to strengthen their advanced Generative AI capabilities in LiechtensteinLiechtenstein
Enroll in Advanced Retrieval-Augmented Generation (RAG) training in LiechtensteinLiechtenstein to develop advanced RAG expertise and prepare for modern enterprise AI and LLM application development
Target audience
- RAG Developers
- Generative AI Developers
- LLM Engineers
- AI and Machine Learning Engineers
- NLP Engineers
- AI Solutions Architects
- Software Developers
- Data Scientists and Data Engineers
- AI Solutions Consultants
- Enterprise AI Professionals
- Technology Professionals and AI Enthusiasts
- AI and Technology Leaders
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Professional & Training Experience: 5+ Years
- Course Training Experience: 150+ Hours of training focused on Advanced RAG, retrieval optimization, embeddings, vector databases, hybrid search, reranking, query transformation, context optimization, LLM integration, and RAG evaluation.
- Candidates Trained: 200+ candidates, including AI professionals, software developers, machine learning engineers, data professionals, IT professionals, technical architects, and Generative AI enthusiasts.
- Training Delivery Experience: Experienced in instructor-led online and classroom training with advanced demonstrations, hands-on labs, coding exercises, technical assignments, case studies, and project-based learning.
- Advanced RAG Expertise: Practical experience with advanced RAG architectures, document processing, semantic and hierarchical chunking, metadata enrichment, embeddings, retrieval strategies, context management, reranking, and LLM-based response generation.
- Hybrid Search Experience: Hands-on experience combining semantic vector search with keyword-based retrieval to improve search quality for complex business and enterprise queries.
- Query Optimization: Experience implementing query rewriting, query expansion, query decomposition, contextual retrieval, and other techniques for improving retrieval effectiveness.
- Reranking & Context Optimization: Practical knowledge of reranking retrieved results, removing irrelevant information, reducing duplicate context, and selecting high-quality information for LLM processing.
- Vector Database Experience: Experience working with vector database concepts, indexing, similarity search, metadata filtering, retrieval optimization, and scalable knowledge retrieval architectures.
- RAG Evaluation & Optimization: Experience evaluating retrieval relevance, response accuracy, context quality, groundedness, completeness, and overall RAG performance, followed by iterative optimization.
- LLM Integration: Practical experience integrating LLMs with advanced retrieval pipelines to build context-aware, knowledge-grounded, and enterprise-ready AI applications.
- Business & Enterprise Use Cases: Training and project experience applying advanced RAG to business knowledge management, customer support, document intelligence, enterprise search, product information, technical documentation, and internal knowledge systems.
- Security & Responsible AI: Training experience covering prompt injection, sensitive-data protection, access control, data leakage, hallucination management, source validation, and human-in-the-loop review.
- Hands-On Project Guidance: Experience guiding learners through advanced RAG projects involving document ingestion, chunking, embeddings, hybrid retrieval, query transformation, reranking, LLM integration, evaluation, and optimization.
- Assessment Experience: Experienced in evaluating learners through technical assignments, practical labs, scenario-based exercises, RAG implementation tasks, performance evaluation, and project demonstrations.
Documented learner outcomes
- Problem Statement – A business manages large and continuously growing volumes of product documents, policies, technical manuals, customer records, reports, knowledge articles, FAQs, and internal business information. A basic RAG system may struggle with complex queries, irrelevant retrieval, incomplete context, duplicate information, large documents, and changing business data. Inaccurate retrieval can lead to poor LLM responses and reduce trust in AI-generated information. The challenge is to design an advanced RAG solution that can improve retrieval accuracy, context quality, response reliability, scalability, and security for complex business use cases.
- How We Overcame the Problem – Learners analysed the limitations of a basic RAG implementation and designed an advanced retrieval architecture using improved document processing, hierarchical and strategic chunking, metadata enrichment, embedding optimization, hybrid search, reranking, query transformation, contextual retrieval, and retrieval evaluation techniques. Learners experimented with different retrieval strategies, measured results, refined the RAG pipeline, and introduced validation and responsible AI practices to improve the quality and reliability of business responses.
- Overall Implementation – Learners implemented an advanced AI knowledge workflow covering business requirement → data collection → document preprocessing → advanced chunking → metadata enrichment → embedding generation → vector and keyword indexing → hybrid retrieval → query enhancement → reranking → context optimization → LLM response generation → evaluation → refinement → human validation → final business output.
- Advanced Document Processing – Learners processed complex business documents containing structured and unstructured information and applied appropriate preprocessing, cleaning, segmentation, metadata extraction, and document organization techniques.
- Advanced Chunking Strategies – Learners experimented with different chunking approaches, including fixed-size, semantic, hierarchical, and context-aware chunking, to determine how document structure affects retrieval quality and downstream LLM responses.
- Embedding Optimization – Learners evaluated embedding approaches and selected suitable embedding strategies based on business content, query types, semantic similarity, retrieval accuracy, and application requirements.
- Hybrid Search Implementation – Learners combined semantic vector search with traditional keyword-based retrieval to improve results for business queries containing both conceptual information and specific terms, names, identifiers, or product references.
- Metadata-Based Retrieval – Learners enriched knowledge chunks with metadata such as document type, category, date, department, product, and business context, enabling more precise filtering and retrieval.
- Query Transformation – Learners implemented techniques for improving complex user queries by rewriting, expanding, decomposing, or transforming questions into retrieval-friendly formats before searching the knowledge base.
- Reranking and Context Optimization – Learners applied reranking techniques to prioritise the most relevant retrieved information and optimized the final context supplied to the LLM to reduce irrelevant or redundant content.
- Multi-Step and Complex Query Handling – Learners designed workflows capable of handling complex business questions that require multiple retrieval steps, information sources, or reasoning stages before generating a final response.
- Advanced RAG Response Generation – Learners combined high-quality retrieved context with structured prompts and LLMs to generate grounded responses, summaries, reports, recommendations, and business insights.
- RAG Evaluation and Performance Optimization – Learners evaluated retrieval and generation quality using relevant performance criteria such as retrieval relevance, context accuracy, response correctness, completeness, groundedness, and overall usefulness. They refined the RAG pipeline based on evaluation results.
- Advanced RAG Security and Responsible AI – Learners addressed risks including sensitive-data exposure, unauthorised information retrieval, prompt injection, data leakage, hallucinations, incorrect source usage, and inappropriate AI-generated recommendations.
- Human-in-the-Loop Validation – Learners incorporated business-user review and validation for critical outputs, ensuring that retrieved information and AI-generated responses are verified before being used for customer communication or important business decisions.
- Evidence of Learning – Learners produced advanced document-processing workflows, chunking experiments, metadata strategies, embedding configurations, hybrid-search implementations, query-transformation examples, reranking results, retrieval evaluation reports, optimized RAG pipelines, prompt templates, response comparisons, security controls, and project documentation as evidence of their advanced RAG implementation capabilities.
- Overall Learner Outcome – By completing the implementation, learners demonstrated the ability to analyse advanced RAG requirements, optimize document processing and chunking, implement embeddings and hybrid retrieval, apply metadata filtering and query transformation, use reranking and context optimization, handle complex business queries, evaluate RAG performance, improve response quality, address security risks, and implement scalable and responsible RAG solutions for realistic business scenarios.
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Advanced RAG combines information retrieval with generative AI to produce more accurate, relevant, and context-aware responses. |
AI developers, software engineers, ML professionals, data professionals, and GenAI practitioners can benefit from this course.
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