GenAI for .NET: Build LLM Apps with OpenAI and Ollama
About this course
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the power of AI in your .NET applications with this hands-on course. You'll gain practical knowledge of Large Language Models (LLMs), Small Language Models (SLMs), and prompt engineering, empowering you to build intelligent applications for chat, text analysis, and search. Explore real-world projects that combine the latest OpenAI models and Ollama for local AI execution, giving you a complete understanding of both cloud-based and on-premise solutions. The course guides you step-by-step through setting up your .NET AI environment, integrating AI frameworks like Microsoft.Extensions.AI, working with vector databases, and implementing retrieval-augmented generation (RAG) applications. You'll develop AI chat apps, text completion tools, semantic and vector search apps, and even image analysis applications, with hands-on exercises for each concept. You'll also learn to deploy and extend AI solutions using GitHub-hosted models, Azure AI services, and local Ollama models. Projects culminate in a fully functional EShop vector search application, integrating semantic search, chat capabilities, and advanced AI services, giving you both technical depth and practical experience. This course is perfect for .NET developers, AI enthusiasts, and software engineers looking to expand their skills in AI application development. Prerequisites include basic familiarity with .NET and programming concepts. Difficulty is Intermediate, suitable for learners ready to implement AI-powered solutions confidently. By the end of the course, you will be able to build LLM-powered chat apps, implement vector-based search and embeddings, create RAG applications, integrate AI services in .NET, and deploy AI solutions using both cloud and local models.
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