Generative AI Architectures with LLM, Prompt, RAG, Vector DB — 90% OFF Discount Coupon

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

★4.5 out of 5
16,830 students
Created by Mehmet Ozkaya
English
Updated April 2026

Quick Facts — Course Summary

Here's a quick overview of everything you need to know about Generative AI Architectures with LLM, Prompt, RAG, Vector DB before you enroll:

•Course Name: Generative AI Architectures with LLM, Prompt, RAG, Vector DB
•Platform: Udemy
•Instructor: Mehmet Ozkaya
•Coupon Last Verified: April 15, 2026
•Level: All Levels
•Topic: Development
•Subtopic: Generative AI (GenAI)
•Total Time: 7h 30m of video content
•Language: English
•Access Type: Unlimited lifetime access + updates
•Certificate: Included upon completion from Udemy
•Main Skills: Generative AI Model Architectures (Types of Generative AI Models) · Transformer Architecture: Attention is All you Need · Large Language Models (LLMs) Architectures
•Requirements: Basics of Software Developments
•Current Price: $9.99 (was $99.99). You save $90.00 with 90% discount.
•How to Apply: Click the coupon button to activate your discount automatically
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Skills You'll Master

By the end of Generative AI Architectures with LLM, Prompt, RAG, Vector DB, you'll have these practical skills:

✓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.

What You Need Before Starting

Before enrolling in Generative AI Architectures with LLM, Prompt, RAG, Vector DB, make sure you have:

•Basics of Software Developments

About This Udemy Course

The following is the full official course description for Generative AI Architectures with LLM, Prompt, RAG, Vector DB as published on Udemy by instructor Mehmet Ozkaya:

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.

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Is the Generative AI Architectures with LLM, Prompt, RAG, Vector DB Coupon Worth It?

Expert review by Andrew Derek, Lead Course Analyst at CoursesWyn.•Last updated: April 15, 2026.

Based on analysis of the curriculum structure, student engagement metrics, and verified rating data, Generative AI Architectures with LLM, Prompt, RAG, Vector DB is a high-value resource for learners seeking to build skills inDevelopment. Taught by Mehmet Ozkaya on Udemy, the 7h 30m course provides a structured progression from foundational concepts to advanced techniques— making it suitable for learners at all levels. The current coupon reduces the price by 90%, from $99.99 to $9.99, removing the primary financial barrier to enrollment.

✓What We Like (Pros)

  • Verified 90% price reduction makes this course accessible to learners on any budget.
  • Aggregate student rating of 4.5 out of 5 indicates high learner satisfaction.
  • Strong enrollment base with over 16,830 students demonstrates course popularity and trust.
  • Includes an official Udemy completion certificate and lifetime access to all future content updates.

!Keep in Mind (Cons)

The following limitations should be considered before enrolling in Generative AI Architectures with LLM, Prompt, RAG, Vector DB:

  • The depth of Development coverage may be challenging for absolute beginners without the listed prerequisites.
  • Lifetime access is contingent on the continued operation of the Udemy platform.
  • Hands-on projects and quizzes require additional time investment beyond video watch time.
Final Verdict: Worth It
This course offers exceptional value with current pricing
✓

Course Rating Summary

Generative AI Architectures with LLM, Prompt, RAG, Vector DB Course holds an aggregate rating of 4.5 out of 5 based on 16,830 student reviews on Udemy.

4.5
★★★★★
16,830 Verified Ratings
5 stars
75%
4 stars
15%
3 stars
6%
2 stars
2%
1 star
2%

* Rating distribution is approximated from the aggregate score. Sourced from Udemy.

Instructor Profile

The following section provides background information on Mehmet Ozkaya, the instructor responsible for creating and maintaining Generative AI Architectures with LLM, Prompt, RAG, Vector DB on Udemy.

Generative AI Architectures with LLM, Prompt, RAG, Vector DB is taught by Mehmet Ozkaya, a Udemy instructor specializing in Development. For the full instructor biography, professional credentials, and a complete list of their courses, visit the official instructor profile on Udemy.

•Instructor Name: Mehmet Ozkaya
•Subject Area: Development
•Teaching Approach: Practical, project-based instruction focused on real-world application of Development skills.

Frequently Asked Questions

The following questions and answers cover the most common queries about Generative AI Architectures with LLM, Prompt, RAG, Vector DB, its coupon code, pricing, and enrollment process.

About the Author

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Andrew Derek

Lead Course Analyst at CoursesWyn with 8+ years of experience evaluating online learning platforms. I've analyzed 500+ Udemy courses and helped thousands of learners choose the right courses for their career goals.

⭐4.8/5 Rating
✓Trusted by 10K+ Students

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