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

In this course, you'll learn how to Design Generative AI Architectures with integrating AI-Powered S/LLMs into EShop Support Enterprise Applications using Prompt Engineering, RAG, Fine-tuning and Vector DBs.

We will design Generative AI Architectures with below components;

1. Small and Large Language Models (S/LLMs)

2. Prompt Engineering

3. Retrieval Augmented Generation (RAG)

4. Fine-Tuning

5. Vector Databases

We start with the basics and progressively dive deeper into each topic. We'll also follow LLM Augmentation Flow is a powerful framework that augments LLM results following the Prompt Engineering, RAG and Fine-Tuning.

Large Language Models (LLMs) module;

• How Large Language Models (LLMs) works?
• Capabilities of LLMs: Text Generation, Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation
• Generate Text with ChatGPT: Understand Capabilities and Limitations of LLMs (Hands-on)
• Function Calling and Structured Output in Large Language Models (LLMs)
• LLM Models: OpenAI ChatGPT, Meta Llama, Anthropic Claude, Google Gemini, Mistral Mixral, xAI Grok
• SLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Google Gemma, Microsoft Phi 3.5
• Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3
• Interacting OpenAI Chat Completions Endpoint with Coding
• Installing and Running Llama and Gemma Models Using Ollama to run LLMs locally
• Modernizing and Design EShop Support Enterprise Apps with AI-Powered LLM Capabilities
• Develop .NET to integrate LLM models and performs Classification, Summarization, Data extraction, Anomaly detection, Translation and Sentiment Analysis use cases.

Prompt Engineering module;

• Steps of Designing Effective Prompts: Iterate, Evaluate and Templatize
• Advanced Prompting Techniques: Zero-shot, One-shot, Few-shot, Chain-of-Thought, Instruction and Role-based
• Design Advanced Prompts for EShop Support – Classification, Sentiment Analysis, Summarization, Q&A Chat, and Response Text Generation
• Design Advanced Prompts for Ticket Detail Page in EShop Support App w/ Q&A Chat and RAG

Retrieval-Augmented Generation (RAG) module;

• The RAG Architecture Part 1: Ingestion with Embeddings and Vector Search
• The RAG Architecture Part 2: Retrieval with Reranking and Context Query Prompts
• The RAG Architecture Part 3: Generation with Generator and Output
• E2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG Workflow
• Design EShop Customer Support using RAG
• End-to-End RAG Example for EShop Customer Support using OpenAI Playground
• Develop RAG – Retrieval-Augmented Generation with .NET, implement the full RAG flow with real examples using .NET

Fine-Tuning module;

• Fine-Tuning Workflow
• Fine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA, Transfer
• Design EShop Customer Support Using Fine-Tuning
• End-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI Playground

Also, we will discuss

• Choosing the Right Optimization – Prompt Engineering, RAG, and Fine-Tuning

Vector Database and Semantic Search with RAG module

• What are Vectors, Vector Embeddings and Vector Database?
• Explore Vector Embedding Models: OpenAI - text-embedding-3-small, Ollama - all-minilm
• Semantic Meaning and Similarity Search: Cosine Similarity, Euclidean Distance
• How Vector Databases Work: Vector Creation, Indexing, Search
• Vector Search Algorithms: kNN, ANN, and Disk-ANN
• Explore Vector Databases: Pinecone, Chroma, Weaviate, Qdrant, Milvus, PgVector, Redis

Lastly, we will Design EShopSupport Architecture with LLMs and Vector Databases

• Using LLMs and VectorDBs as Cloud-Native Backing Services in Microservices Architecture
• Design EShop Support with LLMs, Vector Databases and Semantic Search
• Azure Cloud AI Services: Azure OpenAI, Azure AI Search
• Design EShop Support with Azure Cloud AI Services: Azure OpenAI, Azure AI Search

This course is more than just learning Generative AI, it's a deep dive into the world of how to design Advanced AI solutions by integrating LLM architectures into Enterprise applications.

You'll get hands-on experience designing a complete EShop application, including LLM capabilities like Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation.

This Udemy coupon unlocks a guided path into Generative AI Architectures with LLM, Prompt, RAG, Vector DB, so you know exactly what outcomes to expect before you even press play.

Mehmet Ozkaya leads this Udemy course in Development, blending real project wins with step-by-step coaching.

The modules are sequenced to unpack Generative AI (GenAI) step by step, blending theory with scenarios you can reuse at work while keeping the Udemy course reviews tone in mind.

Video walkthroughs sit alongside quick-reference sheets, checklists, and practice prompts that make it easy to translate the material into real projects, especially when you grab Udemy discounts like this one.

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