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