Source: https://www.udemy.com/course/generative-ai-architectures-with-llm-prompt-rag-vector-db/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
- 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
- Basics of Software Developments
It’s not my rip
Download Links
Password: cms.ddpanda.org
