Training course · RAG & LLM Applications · C# & .NET

RAG & LLM Applications for C# and .NET Developers

Develop RAG and LLM applications: embeddings, vector databases, chunking, retrieval, evaluation, cost control and data protection.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“RAG & LLM Applications for C# and .NET Developers” is a hands-on course for roles such as C# developers working with ASP.NET Core, Blazor or WPF, Teams migrating applications from .NET Framework to modern .NET and Software architects for .NET and Azure solutions. Rather than learning RAG & LLM Applications in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from C# & .NET.

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.

C# and .NET are firmly established in many organisations: ASP.NET Core APIs, Blazor and WPF front ends, Azure services and line-of-business applications dating back to the .NET Framework era. Migrating from the classic .NET Framework to modern cross-platform .NET in particular ties up significant capacity. AI coding assistants and agents take over much of the mechanical work here: converting project files to SDK style, identifying WCF or Web Forms dependencies, writing xUnit or NUnit tests, optimising Entity Framework Core queries and introducing nullable reference types. To do this well, they need context on solution structure, analyser rules and architectural guidelines. This course shows you how to use agents in Visual Studio, VS Code or Rider and on the command line.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • C# developers working with ASP.NET Core, Blazor or WPF
  • Teams migrating applications from .NET Framework to modern .NET
  • Software architects for .NET and Azure solutions
  • Developers of line-of-business applications in industry and SMEs

Typical challenges we address

  • Costly migration of .NET Framework applications built on WCF, Web Forms or ageing NuGet packages
  • Large solutions with many projects, circular dependencies and little test coverage
  • Performance issues caused by inefficient Entity Framework queries
  • Inconsistent handling of nullability, async/await and dependency injection

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.
  • Use AI agents for .NET migrations, refactorings and test generation across large solutions.
  • Optimise Entity Framework Core data access and ASP.NET Core APIs with AI support.
  • Define analyser rules, conventions and review criteria that generated C# code meets reliably.

Course outline

Day 1

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

Day 2

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
Module 6 Practice lab: C# & .NET
  • Migrating to modern .NET
  • Writing xUnit and NUnit tests
  • Optimising Entity Framework Core
  • Introducing nullable reference types
  • Building ASP.NET Core APIs
  • Code analysis and refactoring
Module 7 Data protection, quality & transfer to daily work
  • Clarify whether source code may be sent to cloud models and which tenant or region settings apply, and keep connection strings and secrets from User Secrets or Key Vault out of prompts. New NuGet packages should be checked for licence, provenance and vulnerabilities – not least in view of the obligations under the Cyber Resilience Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Migrating to modern .NET

The agent analyses projects using the upgrade tooling and compiler output, converts csproj files to SDK style, replaces incompatible APIs and proposes alternatives to WCF or System.Web until the solution builds and tests pass.

Writing xUnit and NUnit tests

Services, controllers and domain logic get unit tests with Moq or NSubstitute plus integration tests using WebApplicationFactory. The AI covers edge cases and sticks to your existing test conventions.

Optimising Entity Framework Core

The AI analyses generated SQL, spots N+1 problems, missing AsNoTracking calls or awkward Includes, and creates migrations and projections for faster queries. Every change is backed by the resulting SQL.

Introducing nullable reference types

Nullable is enabled project by project while the AI works through compiler warnings, adds guard clauses and annotations and makes implicit assumptions in the code explicit.

Building ASP.NET Core APIs

Specifications become minimal APIs or controllers with validation, ProblemDetails error handling, authentication and OpenAPI documentation, consistent with your existing layered architecture. Integration tests safeguard each new endpoint.

Code analysis and refactoring

Roslyn analyser findings are fixed systematically, oversized classes split, async anti-patterns such as .Result removed and dependency injection registrations made consistent. Build, analysers and existing tests confirm that behaviour is unchanged.

Possible hands-on projects

Minimal API service

An ASP.NET Core service using minimal APIs, EF Core, health checks, structured logging and integration tests, built end to end by an AI agent from a specification.

Migration inventory

A console tool that scans a solution, lists target frameworks, NuGet packages and incompatible APIs, and produces a prioritised migration plan.

Blazor admin UI

A small Blazor application for managing master data, with form validation, filterable tables and integration with an existing REST API.

Prerequisites

Solid programming skills in Python (or TypeScript as an alternative), basic knowledge of databases and REST APIs, and some experience with LLM APIs. Good C# skills, experience with ASP.NET Core or .NET desktop development, and familiarity with Visual Studio, Rider or VS Code and Git.

Technical requirements

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.

Data protection & compliance

Clarify whether source code may be sent to cloud models and which tenant or region settings apply, and keep connection strings and secrets from User Secrets or Key Vault out of prompts. New NuGet packages should be checked for licence, provenance and vulnerabilities – not least in view of the obligations under the Cyber Resilience Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Wed, 2 December 2026 – Thu, 3 December 2026 Augsburg Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Mon, 11 January 2027 – Tue, 12 January 2027 Kaiserslautern Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Online Request
  • Wed, 10 February 2027 – Thu, 11 February 2027 Bremen Request

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.

Does AI really help with migrating from .NET Framework?

Yes, especially with the mechanical work: converting project files, finding incompatible APIs, suggesting replacement libraries and fixing build errors iteratively. Fundamental architecture decisions, such as replacing WCF with gRPC or REST, remain with the team. On the course you practise breaking such migrations into small, verifiable steps.

Do AI agents work with Visual Studio?

Many AI assistants integrate directly with Visual Studio, VS Code or Rider, while others run in the terminal alongside the dotnet CLI. The methods taught are tool-neutral: you learn to provide context, scope tasks and safeguard results through builds, analysers and tests, regardless of your IDE.

How much does “RAG & LLM Applications for C# and .NET Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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