What you'll learn:
- 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
In this course, you'll learn how to Design Generative AIArchitectures 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
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
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 Customer Support application, including LLM capabilities like Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation.
