Vibe & agentic coding
RAG & LLM Applications training courses
Develop RAG and LLM applications: embeddings, vector databases, chunking, retrieval, evaluation, cost control and data protection.
Retrieval-augmented generation connects language models to your own knowledge: manuals, contracts, tickets, wikis or product data are made searchable and handed to the model as context at runtime. The result is assistants that give grounded answers with citations instead of hallucinating. In this two-day course you build a complete RAG pipeline, from document preparation through embeddings and a vector database to answer generation, and you learn which levers actually determine quality.
Day two takes you from prototype to robust application. You compare chunking strategies, combine semantic and lexical search, apply reranking, metadata filters and query rewriting, and measure the effect with systematic evaluations. You also cover prompt caching, cost control, latency optimisation, document-level access control and privacy-compliant operation with an eye on GDPR and the EU AI Act. By the end you know when classic RAG is enough, when a large context window or agentic retrieval is the better option, and how to justify your architecture choices.
Core content of every RAG & LLM Applications course
Module 1 Foundations of RAG and LLM applications
- Anatomy of a RAG pipeline: ingestion, indexing, retrieval, generation
- Understanding embeddings: semantic similarity, dimensions, model selection
- Weighing RAG against fine-tuning and long context windows
- Structured outputs, citations and dealing with hallucinations
Module 2 Document preparation and chunking
- Reliably extracting PDFs, Office documents, HTML and tables
- Chunking strategies: fixed size, structure-aware, semantic, with overlap
- Enriching metadata and generating contextual summaries per chunk
- Incremental updates and deletion of documents in the index
Module 3 Vector databases and retrieval strategies
- Comparing vector databases and extensions such as pgvector, Qdrant, Weaviate or OpenSearch
- Hybrid search combining vector and full-text search with BM25
- Reranking, metadata filtering and query rewriting
- Agentic retrieval: letting the model decide on search steps and tools
Module 4 Evaluation and quality assurance
- Building and maintaining test sets from real user questions
- Retrieval metrics such as recall and precision, and answer metrics such as faithfulness
- Using and calibrating LLM-as-a-judge sensibly
- Regression testing when chunking, model or prompt changes
Module 5 Operations, cost and data protection
- Prompt caching, batch processing and model choice to cut costs
- Optimising latency and streaming responses
- Document-level permissions, tenant isolation and protection against prompt injection
- Data protection: data residency, processing agreements, deletion policies, GDPR and the EU AI Act
What you will be able to do
- You build a complete RAG pipeline from document preparation to answers with citations.
- You choose embedding model, chunking strategy and vector database on solid grounds.
- You improve retrieval quality with hybrid search, reranking and metadata filters.
- You measure application quality with systematic evaluations and detect regressions.
- You run LLM applications cost-effectively, with proper access control and in line with data protection rules.
All 29 RAG & LLM Applications courses
Development & technology (29)
- RAG & LLM Applications for AI Engineers
- RAG & LLM Applications for API Developers
- RAG & LLM Applications for C# and .NET Developers
- RAG & LLM Applications for C/C++ and Embedded Developers
- RAG & LLM Applications for Citizen Developers
- RAG & LLM Applications for Data Engineers
- RAG & LLM Applications for Data Scientists and ML Engineers
- RAG & LLM Applications for DevOps and Cloud Teams
- RAG & LLM Applications for Go Developers
- RAG & LLM Applications for Internal Tools and Automation
- RAG & LLM Applications for Java and Spring Developers
- RAG & LLM Applications for JavaScript and TypeScript Developers
- RAG & LLM Applications for Kubernetes and Microservices Teams
- RAG & LLM Applications for Legacy Modernisation
- RAG & LLM Applications for Startups and MVP Development
- RAG & LLM Applications for Mobile Developers
- RAG & LLM Applications for Node.js Back-End Developers
- RAG & LLM Applications for PHP Developers
- RAG & LLM Applications for Python Developers
- RAG & LLM Applications for React and Front-End Developers
- RAG & LLM Applications for Rust Developers
- RAG & LLM Applications for SAP ABAP Developers
- RAG & LLM Applications for SQL and Database Professionals
- RAG & LLM Applications for Salesforce Developers
- RAG & LLM Applications for Security and DevSecOps Teams
- RAG & LLM Applications for Software Architects
- RAG & LLM Applications for Tech Leads and Engineering Managers
- RAG & LLM Applications for Test Automation and QA
- RAG & LLM Applications for Web Developers
RAG & LLM Applications training near you
Frequently asked questions
When is RAG better than fine-tuning?
RAG is the right choice when a model needs access to current, extensive or frequently changing knowledge and should cite its sources. Fine-tuning mainly changes style, format or specialised behaviour. In many projects RAG is the faster, cheaper and more controllable route, sometimes complemented by fine-tuning for niche tasks.
Which vector database should we use?
That depends on data volume, existing infrastructure, filtering needs and operating model. If you already run PostgreSQL, pgvector often takes you a long way, while specialised systems shine with large datasets and hybrid search. In the course you compare several options hands-on and derive criteria for your own decision.
How can RAG be run in a GDPR-compliant way?
Key factors are choosing providers with suitable data residency and a data processing agreement, enforcing document-level permissions, having a deletion policy for the index and logs, and minimising personal data. For highly sensitive data, self-hosted models are also an option. We work through these choices using realistic architectures.
Prerequisites
Solid programming skills in Python (or TypeScript as an alternative), basic knowledge of databases and REST APIs, and some experience with LLM APIs.
A laptop with Python, Git, Docker and a code editor, plus API access to at least one LLM and embedding provider. Credentials for the exercises can be provided if needed.
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