Get Generative AI Architectures with LLM, Prompt, RAG, Vector DB with 90% OFF Udemy Coupon
Design and Integrate AI-Powered S/LLMs into Enterprise Apps using Prompt Engineering, RAG, Fine-Tuning and Vector DBs.
Key Takeaways — Course Overview
The following summarizes all verified data points for Generative AI Architectures with LLM, Prompt, RAG, Vector DB, including pricing, duration, instructor, and coupon validity. All data is sourced directly from Udemy and verified by CoursesWyn on .
Course Title: Generative AI Architectures with LLM, Prompt, RAG, Vector DB
Platform: Udemy (listed via CoursesWyn)
Instructor: Mehmet Ozkaya
Coupon Verified:
Difficulty Level: All Levels
Category: Development
Subcategory: Generative AI (GenAI)
Duration: 7h 30m of on-demand video
Language: English
Access: Lifetime access to all course lectures and updates
Certificate: Official certificate of completion issued by Udemy upon finishing all course requirements
Top Learning Outcomes: Students who complete Generative AI Architectures with LLM, Prompt, RAG, Vector DB will be able to: Generative AI Model Architectures (Types of Generative AI Models) · Transformer Architecture: Attention is All you Need · Large Language Models (LLMs) Architectures
Prerequisites: Basics of Software Developments
Price: $9.99 with coupon / Regular Udemy price: $99.99. Applying this coupon saves you $90.00 (90% OFF).
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What You'll Learn
Completing Generative AI Architectures with LLM, Prompt, RAG, Vector DB gives you the following verified skills and competencies in Development:
- 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
The following background knowledge and tools are recommended before starting Generative AI Architectures with LLM, Prompt, RAG, Vector DB. Students without these prerequisites may still enroll but should expect a steeper learning curve.
- 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. It covers the curriculum structure, teaching methodology, and topic scope for this Development course.
- Small and Large Language Models (S/LLMs)
- Prompt Engineering
- Retrieval Augmented Generation (RAG)
- Fine-Tuning
- Vector Databases
- 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.
- 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
- 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 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
- Choosing the Right Optimization – Prompt Engineering, RAG, and Fine-Tuning
- 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
- 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
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Is This Course Worth It?
Expert review by Andrew Derek, Lead Course Reviewer at CoursesWyn. Last updated: .
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 in Development. Taught by Mehmet Ozkaya on Udemy, the 7h 30m course provides a structured progression from foundational concepts to advanced Generative AI (GenAI) 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)
The following advantages were identified:
- Verified 90% price reduction makes this course accessible on any budget.
- Aggregate student rating of 4.5 out of 5 indicates high satisfaction.
- Includes an official Udemy completion certificate and lifetime access.
Keep in Mind (Cons)
The following limitations should be considered:
- The depth of Generative AI (GenAI) coverage may be challenging for newcomers.
- Lifetime access is contingent on the Udemy platform's operation.
- Hands-on projects require additional time beyond video watch time.
"Given the 90% price reduction and verified 4.5-star rating, Generative AI Architectures with LLM, Prompt, RAG, Vector DB represents one of the strongest value propositions currently available in Development. Enrollment is recommended while this coupon remains active."
Course Rating Summary
Generative AI Architectures with LLM, Prompt, RAG, Vector DB holds an aggregate rating of 4.5 out of 5 based on 11,710 student reviews on Udemy. The distribution below shows the approximate percentage of students who gave each star rating.
4.5
11,710 Verified Ratings
* Rating distribution is approximated from the aggregate score. Sourced from Udemy. Last verified: .
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.
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Instructor Name: Mehmet Ozkaya
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Subject Area: Development
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Teaching Approach: Practical, project-based instruction focused on real-world application of Generative AI (GenAI) skills.
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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. All answers are based on verified data from Udemy as of .
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Andrew Derek
Expert ReviewerAndrew Derek is a lead editor and course analyst at CoursesWyn with over 8 years of experience in online education and digital marketing. He meticulously audits every Udemy coupon and course syllabus to ensure students get the highest quality learning materials at the best possible price.
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