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

Design and Integrate AI-Powered S/LLMs into Enterprise Apps using Prompt Engineering, RAG, Fine-Tuning and Vector DBs
4.4 ★★★★★6,648 studentsCreated by Mehmet OzkayaLast updated Dec 10, 2025🌐 English

What you'll learn

✓Generative AI Model Architectures (Types of Generative AI Models)
✓Transformer Architecture: Attention is All you Need
✓Large Language Models (LLMs) Architectures
✓Text Generation, Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search
✓Generate Text with ChatGPT: Understand Capabilities and Limitations of LLMs (Hands-on)
✓Function Calling and Structured Outputs in Large Language Models (LLMs)
✓LLM Providers: OpenAI, Meta AI, Anthropic, Hugging Face, Microsoft, Google and Mistral AI
✓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
✓How to Choose LLM Models: Quality, Speed, Price, Latency and Context Window
✓Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3
✓Installing and Running Llama and Gemma Models Using Ollama
✓Modernizing Enterprise Apps with AI-Powered LLM Capabilities
✓Designing the 'EShop Support App' with AI-Powered LLM Capabilities
✓Advanced Prompting Techniques: Zero-shot, One-shot, Few-shot, COT
✓Design Advanced Prompts for Ticket Detail Page in EShop Support App w/ Q&A Chat and RAG
✓The RAG Architecture: Ingestion with Embeddings and Vector Search
✓E2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG Workflow
✓End-to-End RAG Example for EShop Customer Support using OpenAI Playground
✓Fine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA, Transfer
✓End-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI Playground
✓Choosing the Right Optimization – Prompt Engineering, RAG, and Fine-Tuning
✓Vector Database and Semantic Search with RAG
✓Explore Vector Embedding Models: OpenAI - text-embedding-3-small, Ollama - all-minilm
✓Explore Vector Databases: Pinecone, Chroma, Weaviate, Qdrant, Milvus, PgVector, Redis
✓Using LLMs and VectorDBs as Cloud-Native Backing Services in Microservices Architecture
✓Design EShop Support with LLMs, Vector Databases and Semantic Search
✓Design EShop Support with Azure Cloud AI Services: Azure OpenAI, Azure AI Search
✓Develop .NET to integrate LLM models and performs Classification, Summarization, Data extraction, Anomaly detection, Translation and Sentiment Analysis use case
✓Develop RAG – Retrieval-Augmented Generation with .NET, implement the full RAG flow with real examples using .NET and Qdrant

Requirements

•Basics of Software Developments

Description

Frequently Asked Questions

Student Feedback

4.4
★★★★★
Course Rating
75%
15%
5%
5%
5%
S
Sarah J.
★★★★★2 weeks ago

This course was absolutely amazing! The instructor explained everything clearly and the projects were very helpful.

M
Michael T.
★★★★1 month ago

Great content, highly recommended for beginners. Just wish there were more practice exercises.

D
David K.
★★★★★2 months ago

Best course on this topic I've taken so far. Worth every penny (even better since I got it for free!).

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Generative AI Architectures with LLM, Prompt, RAG, Vector DB
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This course includes:
  • 📺 7h 30m on-demand video
  • 📱 Access on mobile and TV
  • ♾️ Full lifetime access
  • 🏆 Certificate of completion