| File Name: | Generative AI Architectures with LLM, Prompt, RAG, Vector DB |
| Content Source: | https://www.udemy.com/course/generative-ai-architectures-with-llm-prompt-rag-vector-db |
| Genre / Category: | Ai Courses |
| File Size : | 3.1 GB |
| Publisher: | Mehmet Ozkaya |
| Updated and Published: | September 29, 2025 |
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
DOWNLOAD LINK: Generative AI Architectures with LLM, Prompt, RAG, Vector DB
Generative_AI_Architectures_with_LLM_Prompt_RAG_Vector_DB.part1.rar – 1000.0 MB
Generative_AI_Architectures_with_LLM_Prompt_RAG_Vector_DB.part2.rar – 1000.0 MB
Generative_AI_Architectures_with_LLM_Prompt_RAG_Vector_DB.part3.rar – 1000.0 MB
Generative_AI_Architectures_with_LLM_Prompt_RAG_Vector_DB.part4.rar – 32.9 MB
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