https://github.com/wmeints/effective-llm-applications-workshop
The workshop for my book "Building Effective LLM-Based Applications with Semantic Kernel"
https://github.com/wmeints/effective-llm-applications-workshop
course csharp llm semantic-kernel teaching workshop
Last synced: 6 months ago
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The workshop for my book "Building Effective LLM-Based Applications with Semantic Kernel"
- Host: GitHub
- URL: https://github.com/wmeints/effective-llm-applications-workshop
- Owner: wmeints
- Created: 2025-03-20T07:49:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-15T09:42:49.000Z (about 1 year ago)
- Last Synced: 2025-12-06T20:59:03.577Z (7 months ago)
- Topics: course, csharp, llm, semantic-kernel, teaching, workshop
- Language: TypeScript
- Homepage: https://wmeints.github.io/effective-llm-applications-workshop/
- Size: 488 KB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
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README
# Course: Building Effective LLM-Based Applications with Semantic Kernel
Welcome to the source repository for the workshop "Building Effective LLM-Based
Applications with Semantic Kernel". In this workshop we teach people how to build
LLM-based applications with Semantic Kernel.
---
**NOTICE**
If you're here because you're attending the workshop. Go straight to the website please!
Website: https://wmeints.github.io/effective-llm-applications-workshop
---
## Why this workshop
This hands-on workshop transforms the theoretical knowledge from the book
"Building Effective LLM-Based Applications with Semantic Kernel" into practical
implementation skills. While books provide comprehensive knowledge, workshops offer
immediate application and feedback. This workshop was created to:
- Give developers direct, hands-on experience with LLM application patterns
- Provide structured guidance through common implementation challenges
- Create a collaborative learning environment where participants can share insights
- Transform theoretical concepts into working code you can reference for future projects
## Target audience
This workshop is designed for:
- Software developers who want to integrate LLMs into enterprise applications
- C# developers looking for practical examples beyond the Python-dominated tutorials
- Technical architects designing LLM-powered solutions
- Developers who learn best through hands-on implementation rather than theory alone
**Important** The course materials focus on C# for now. We'll add detailed labs for
Python and Java at a later date!
## Major Learning Goals
By the end of this 3-hour workshop, participants will be able to:
- Set up a Semantic Kernel environment - Configure the essential components for RAG applications
- Process and embed documents - Transform documents into vector embeddings for retrieval
- Implement vector search - Create efficient retrieval mechanisms for relevant content
- Design effective RAG prompts - Craft prompts that properly utilize retrieved context
- Build a complete RAG pipeline - Connect all components into a working domain-specific assistant
## Prerequisites
Participants should have:
- Working knowledge of C# programming
- .NET SDK 9.0 or higher installed
- Docker Desktop or similar containerization tool
- Visual Studio Code or similar code editor
- Azure CLI for working with Azure OpenAI
- GitHub account to access workshop materials
## Workshop Pacing
The workshop is structured as a compact 3-hour session focused exclusively on RAG implementation.
The first module will be more theoretical in nature while the other modules focus on hands-on labs:
| Time | Module | Description |
| --------- | ----------------------------------- | --------------------------------------------------------------------- |
| 0:00-0:45 | Fundamentals & Environment Setup | Overview of LLMS, RAG architecture and configuring Semantic Kernel |
| 0:45-1:30 | Document Processing & Embeddings | Implementing text splitting, embedding generation, and vector storage |
| 1:30-2:45 | Context Integration | Creating prompts that effectively use retrieved information |
| 2:45-3:00 | Q&A and Next Steps | Discussion of advanced techniques and resources for further learning |
Each module includes:
- A brief conceptual explanation
- Step-by-step labs to get hands-on experience
- Working code examples to demonstrate the end results
- Explanation of common pitfalls and solutions
## Supplemental Resources for Facilitators
To prepare for facilitating this workshop:
1. **Required Reading**:
- [Building Effective LLM-Based Applications with Semantic Kernel](https://leanpub.com/effective-llm-applications-with-semantic-kernel/) (focus on the RAG chapter)
- [Microsoft Semantic Kernel Documentation on Memory and Embeddings](https://learn.microsoft.com/en-us/semantic-kernel/memories/)
2. **Environment Preparation**:
- Set up the [GitHub repository](https://github.com/wmeints/effective-llm-applications/) samples for RAG
- Prepare sample documents for embedding demonstrations (You can use [the raw book content](https://github.com/wmeints/effective-llm-applications) for this)
- Ensure you have API keys for OpenAI or Azure OpenAI embeddings and text generation
3. **Discussion Preparation**:
- Prepare examples of effective vs. ineffective RAG implementations
- Review common challenges in text chunking and retrieval
- Be ready to discuss tradeoffs in chunk size, embedding models, and context window utilization
4. **Additional Resources**:
- [Semantic Kernel Memory concepts](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/Concepts/Memory)
- [Azure Cognitive Search integration samples](https://github.com/Azure-Samples/azure-search-openai-demo-csharp)
- [OpenAI Cookbook RAG examples](https://github.com/openai/openai-cookbook/tree/main/examples/vector_databases)
## Getting Started
1. Navigate to the workshop website: https://wmeints.github.io/effective-llm-applications-workshop
2. Follow the setup instructions on the website.
For questions or issues, please create an issue in the [GitHub repository](https://github.com/wmeints/effective-llm-applications/).
## Working on the course materials
This course is being developed as an [Astro](https://astro.build) website. You can find the sources for the
course guides in the [site](./site) directory. The lab content is located in [labs](./labs).
Please make sure to submit issues for things you want to see changed.
---
Workshop materials based on the book "Building Effective LLM-Based Applications with Semantic Kernel" by Willem Meints.