Generative AI Architectures with LLM, Prompt, RAG, Vector DB

This comprehensive architectural guide, created by expert instructor Mehmet Ozkaya, offers an in-depth exploration of integrating Generative AI capabilities into modern enterprise applications. Ideal for software architects, software engineers, and system designers, the course demystifies the complex landscape of Large Language Models (LLMs) and Small Language Models (SLMs), including popular options like OpenAI GPT-4o mini, Meta Llama 3.2, Anthropic Claude, and Google Gemini. Learners will delve deep into the practical implementations of Retrieval-Augmented Generation (RAG) workflows, vector databases such as Pinecone, Chroma, and Qdrant, and advanced prompt engineering techniques including zero-shot, few-shot, and Chain-of-Thought (COT). The curriculum successfully bridges the gap between theoretical AI patterns and real-world cloud-native microservices architecture. By working through highly practical, hands-on scenarios like the EShop Support application, students gain invaluable experience implementing semantic search, model fine-tuning methodologies like PEFT and LoRA, and vector embedding models. Furthermore, the course provides concrete examples of integrating LLMs using the .NET framework, making it a critical learning resource for developers aiming to build secure, scalable, and production-ready AI-powered solutions.