Perth, Australia

Generative AI & RAG

Advanced Retrieval-Augmented Generation (RAG)in Perth

40 Hours/5 daysLive virtual trainingAdvanced

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Perth, Australia. Dates, seating capacity, facilities and the final venue are confirmed before booking.

Learning in Perth, Australia

City details

Perth combines a high-quality learning environment, cultural diversity and active professional communities.

City speciality

Education, liveability and business communities

Classroom availability and venue

Build your understanding

Explore Advanced Retrieval-Augmented Generation (RAG)

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 Perth, Australia, 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

View the complete course information

Your learning location

Learning from Perth

Perth combines a high-quality learning environment, cultural diversity and active professional communities.

Make your learning count

City-page course benefits

Master Advanced RAG Techniques – Develop advanced knowledge of Retrieval-Augmented Generation architectures and retrieval workflows in PerthAustralia
Optimize Information Retrieval – Learn query optimization, hybrid search, reranking, filtering, and advanced retrieval strategies
Build Scalable RAG Applications – Understand techniques for designing scalable and production-ready RAG solutions in PerthAustralia
Improve Retrieval Accuracy – Apply contextual retrieval, semantic search, metadata filtering, and reranking to improve relevant information retrieval
Enhance LLM Integration – Learn advanced methods for connecting retrieved context with Large Language Models and improving response generation
Work with Advanced Vector Search – Develop practical knowledge of vector databases, indexing, similarity search, and hybrid retrieval
Evaluate RAG Performance – Learn methods for evaluating retrieval quality, response relevance, faithfulness, and overall RAG performance
Prepare for Advanced AI Roles – Strengthen skills relevant to RAG Developer, Generative AI Engineer, LLM Engineer, and AI Solutions roles in PerthAustralia

Course curriculum

Module 1: Advanced RAG Architecture

Understanding advanced Retrieval-Augmented Generation concepts and enterprise RAG workflows in PerthAustralia
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 PerthAustralia
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 PerthAustralia
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 PerthAustralia
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 PerthAustralia
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 PerthAustralia
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 PerthAustralia
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 PerthAustralia
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 PerthAustralia
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 PerthAustralia
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

Exam details

Exam Level: Intermediate–Advanced – Retrieval-Augmented Generation (RAG) in PerthAustralia
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 PerthAustralia
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 PerthAustralia

Plan your learning

Who this course is for

RAG Developers – Professionals looking to advance their skills in retrieval pipelines, reranking, hybrid search, and enterprise RAG development in PerthAustralia Generative AI Developers – Developers seeking advanced expertise in LLM applications, RAG architectures, and knowledge-driven AI solutions LLM Engineers – Professionals working with Large Language Models who want to develop advanced retrieval and context-management capabilities in PerthAustralia AI and Machine Learning Engineers – AI professionals interested in building and optimizing advanced RAG and Generative AI applications NLP Engineers – Natural Language Processing professionals seeking practical knowledge of semantic…

Before you begin

RAG Fundamentals – Understanding of basic RAG architecture, retrieval workflows, and Generative AI concepts is recommended for learners in PerthAustralia 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 PerthAustralia 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…

Learning 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 –…

Prefer a course discussion? Schedule a meeting

Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course 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…

Continue exploring

Insights for Advanced Retrieval-Augmented Generation (RAG)

Learning insight 01

Advanced Retrieval-Augmented Generation (RAG): Complete Guide to Advanced RAG, Vector Search, Reranking, Hybrid Retrieval & LLMs

Retrieval-Augmented Generation (RAG) has become an important architecture for building reliable Generative AI applications that can use external and enterprise-specific information. While basic RAG can retrieve relevant documents and provide them to a Large Language Model (LLM), Advanced Retrieval-Augmented Generation…

Read the full article

Explore this course locally

Sydney Melbourne Brisbane Adelaide