معماری هوش مصنوعی مولد با LLM, Prompt, RAG, Vector DB

عنوان دوره : Udemy – Generative AI Architectures with LLM, Prompt, RAG, Vector DB

سطح : متوسط

زمان کل دوره : 7 ساعت و 20 دقیقه

تعداد فايل ها : 89

سال آموزش : 2026

فایل تمرینی : دارد: DOCX (2)، JSONL (2)، MD (6)، PDF (1)، TXT (3)

مربی دوره : Mehmet Ozkaya

نرم افزار : –

زیرنویس انگلیسی : دارد

Udemy – 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;

Small and Large Language Models (S/LLMs)

Prompt Engineering

Retrieval Augmented Generation (RAG)

Fine-Tuning

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.

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

Who this course is for:
– Beginner to integrate AI-Powered LLMs into Enterprise Apps