In this video, we explore the power of reasoning models like DeepSeek-R1 and how they enhance Retrieval-Augmented Generation (RAG) in .NET applications. Traditional language models excel at retrieving and summarizing information, but reasoning models take it a step further by improving logical inferences, contextual understanding, and structured outputs.
We start with a quick introduction to DeepSeek-R1, a state-of-the-art model now available in Azure AI Foundry and GitHub Models. Then, we dive into a hands-on .NET console application, highlighting the difference between performing RAG searches using a standard language model and an advanced reasoning model like DeepSeek-R1.
Key benefits of integrating reasoning models in AI applications:
✅ Better Contextual Understanding – Generates more relevant and logical responses
✅ Improved Decision-Making – Helps AI go beyond just retrieving facts to applying reasoning
✅ Enhanced RAG Pipelines – More accurate answers when working with complex knowledge bases
Resources
- DeepSeek-R1 in Azure AI Foundry and GitHub Models: https://azure.microsoft.com/en-us/blog/deepseek-r1-is-now-available-on-azure-ai-foundry-and-github/
- GitHub Demo Repository: https://github.com/elbruno/deepseek-rag-ollama
Watch the video and see how to bring more intelligence to your .NET AI apps!