Course Overview
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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Course curriculum
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What are Large Language Models (LLMs) ?
179 -
How Large Language Models (LLMs) works?
207 -
What is Token And Tokenization ?
228 -
How LLMs Use Tokens
154 Xem trước -
Capabilities of LLMs: Text Generation, Summarization, Q&A, Classification
232 Xem trước -
Limitations of Large Language Models (LLMs)
176 -
Generate Text with ChatGPT: Understand Capabilities and Limitations of LLMs
453 -
LLM Settings: Temperature, Max Tokens, Stop sequences, Top P, Frequency Penalty
397 -
Function Calling in Large Language Models (LLMs)
301 -
Structured Output in Large Language Models (LLMs)
265 -
What are Small Language Models (SLMs) ? Use Cases / How / Why / When
220 -
LLM Quiz
-
Senior .NET Developer Technical Interview: Core LLM Concepts
0
-
LLM Providers: OpenAI, Meta AI, Anthropic, Hugging Face, Microsoft, Google
299 -
LLM Models: OpenAI ChatGPT, Meta Llama, Anthropic Claude, Google Gemini, Mistral
389 -
SLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Gemma, Phi-3
310 -
How to Choose LLM Models: Quality, Speed, Price, Latency and Context Window
338 -
Open Source vs Proprietary Models
193 -
Hugging Face - The GitHub of Machine Learning Models
482 -
LLM Interaction Types: No-Code (ChatUI) or With-Code (API Keys)
221 -
Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3
429 -
Interacting OpenAI Chat Completions Endpoint with Coding
589 -
Ollama – Run LLMs Locally
180 -
Installing and Running Llama and Gemma Models Using Ollama
469 -
Modernizing Enterprise Apps with AI-Powered LLM Capabilities
211 -
Designing the 'EShop Support App' with AI-Powered LLM Capabilities
344 -
LLMs Augmentation Flow: Prompt Engineering -> RAG -> Fine tunning -> Trained
497 -
Presenting the Company's Generative AI Strategy to the CIO
0
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What is Prompt ?
179 -
Elements and Roles of a Prompt
379 -
What is Prompt Engineering ?
176 -
Steps of Designing Effective Prompts: Iterate, Evaluate and Templatize
336 -
Advanced Prompting Techniques
94 -
Zero-Shot Prompting
125 -
One-shot Prompting
154 -
Few-shot Prompting
137 -
Chain-of-Thought Prompting
146 -
Instruction-based and Role-based Prompting
218 -
Design Advanced Prompts for EShop Support – Classification, Sentiment Analysis
390 -
Design Advanced Prompts for Ticket Detail Page in EShop Support App w/ Q&A Chat
359 -
Test Prompts for Eshop Support Customer Ticket w/ Playground
549 -
Prompt Engineering Quiz
-
Troubleshooting and Improving an AI Feature with Advanced Prompting
0
-
Introduction and Create .NET Solution for Developing AI Projects
146 -
GitHub Models – Run LLMs Free
255 -
Create Access Token for GitHub Models
200 -
Text Completion LLM with GitHub Models OpenAI gpt-5-mini model
570 -
Classification, Summarization, Sentiment Analysis LLM Other Use Cases
356 -
Build AI Chat App with .NET and gpt-5-mini model
515
-
What is Retrieval-Augmented Generation (RAG) ?
253 -
Why Need Retrieval-Augmented Generation (RAG) ? Why is RAG Important?
188 -
How Does Retrieval-Augmented Generation (RAG) Work?
332 -
The RAG Architecture Part 1: Ingestion with Embeddings and Vector Search
253 -
The RAG Architecture Part 2: Retrieval with Reranking and Context Query Prompts
184 -
The RAG Architecture Part 3: Generation with Generator and Output
97 -
E2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG Workflow
275 -
Challenges and Key Considerations of Using RAG -- Retrieval-Augmented Generation
239 -
Design EShop Customer Support using RAG
317 -
End-to-End RAG Example for EShop Customer Support using OpenAI Playground
182 -
RAG Quiz
-
Defending Your RAG Architecture in a Technical Review
0
-
What is Fine-Tuning ?
271 -
Why Need Fine-Tuning ?
184 -
When to Use Fine-Tuning ?
146 -
How Does Fine-Tuning Work?
416 -
Fine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA
283 -
Challenges and Key Considerations of Fine-Tuning
258 -
Design EShop Customer Support Using Fine-Tuning
385 -
End-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI Playground
506 -
Fine-Tuning Quiz
-
Proposing a Fine-Tuning Strategy for Enhanced AI Performance
0
-
What is a Vector Database?
289 -
What are Vectors and Vector Embeddings ?
231 -
Explore Vector Embedding Models: OpenAI - text-embedding-3-small, Ollama minilm
348 -
Semantic Meaning and Similarity Search: Cosine Similarity, Euclidean Distance
270 -
How Vector Databases Work: Vector Creation, Indexing, Search
408 -
Vector Search Algorithms: kNN, ANN, and Disk-ANN
280 -
Use Cases and Applications of Vector Databases
113 -
Explore Vector Databases: Pinecone, Chroma, Weaviate, Qdrant, Milvus, PgVector
495 -
Vector Database Quiz
-
Selecting the Right Vector Database for a Large-Scale Project
0
-
Using LLMs and VectorDBs as Cloud-Native Backing Services in Microservices
269 -
Design EShop Support with LLMs, Vector Databases and Semantic Search
590 -
Azure Cloud AI Services: Azure OpenAI, Azure AI Search
336 -
Design EShop Support with Azure Cloud AI Services: Azure OpenAI, Azure AI Search
291 -
Design EShop Quiz
Requirment
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Basics of Software Developments
Outcomes
-
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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