Generative AI Architectures with LLM, Prompt, RAG, Vector DB

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 EngineeringRetrieval Augmented Generation (RAG)Fine-TuningVector DatabasesWe 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 GenerationGenerate 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 GrokSLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Google Gemma, Microsoft Phi 3.5Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3Interacting OpenAI Chat Completions Endpoint with CodingInstalling and Running Llama and Gemma Models Using Ollama to run LLMs locallyModernizing and Design EShop Support Enterprise Apps with AI-Powered LLM CapabilitiesDevelop .NET to integrate LLM models and performs Classificati