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Mehmet Ozkaya's Generative AI Architectures with LLM, Prompt, RAG, Vector DB Course Review

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Design and Integrate AI-Powered S/LLMs into Enterprise Apps using Prompt Engineering, RAG, Fine-Tuning and Vector DBs

Learning Format Online Course

Subcategory Generative AI

Course Price $49.99 (list $9.99)

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About Mehmet Ozkaya

Design and Integrate AI-Powered S/LLMs into Enterprise Apps using Prompt Engineering, RAG, Fine-Tuning and Vector DBs

Who Is Mehmet Ozkaya?

No bio available.

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Total Students

18,710 students

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Course Language

English

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What Do You Learn in Mehmet Ozkaya's Course?

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

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