This video provides a comprehensive look at the latest in generative AI. It starts with Google Gemini Pro 1.5, comparing its features with other models. Learn how to build AI applications using Python, including environment setup and app development. Understand LangChain and its role in creating AI-driven solutions. The video also explains Retrieval Augmented Generation (RAG) and how it improves language models. The course concludes with steps to build a functional LLM app for Android mobile devices.
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🏁 Topics Covered:
00:01:00 Prompt Engineering for ChatGPT
00:01:52 Introduction to Google Gemini 1.5 Pro
00:02:46 Introduction to Google Gemini
00:03:16 Agenda of Google Gemini 1.5 Pro
00:04:06 Versions of Google Gemini
00:11:16 Introduction to Google AI Studio
00:11:39 Gemini Models in Google AI Studio
00:13:19 Tokens in Google AI Studio
00:13:40 Advance Settings in Google AI Studio
00:13:50 Temperature in Google AI Studio
00:14:27 Google AI Studio Left Panel Features
00:15:23 Vertex AI on Google Cloud
00:18:03 General Prompt Demo Google AI Studio
00:20:36 Structured Prompt in Google AI Studio
00:25:56 Model Tuning in Google AI Studio using System Sample
00:28:36 Analysing video Google AI Studio using System Sample
00:29:26 Google Gemini 1.5 Pro Vs Flash
00:31:12 Python Get and Post Method Demo
00:38:27 Python and Google Gemini 1.5 Pro Integration
00:41:32 Get API Key
00:45:24 What is Chatbot?
00:47:30 Types of Chatbots
00:48:41 Applications of Chatbots
00:50:02 Building Chatbot -Theory
00:51:46 Demo on Building Chatbot
01:01:25 Google Gemini 1.5 Pro Vs ChatGPT 4-o versions
01:05:47 Promot Based Trails
01:17:28 Prompt Trail on Problem-Solving and Technical Assistance
01:23:14 Generative AI
01:24:21 Overview of Python and Introduction to GenAI
01:28:04 Development Environment Setup
01:28:05 Generative AI Full course 2024 | All in One Gen AI Tutorial
01:38:51 Introduction to OpenAPI GPT API
01:46:38 Flask ChatGPT App
02:15:27 LangChain Apps
02:26:55 Introduction to LangChain
02:27:23 Agenda of LangChain
02:28:09 How Generative AI will transform our lives in 2023 | Automation with AI tools | ChatGPT | AI Tools
02:31:59 Why LangChain?
02:37:09 Development Environment Setup of LangChain
02:38:48 Demo on Library Installation
02:41:13 LangChain Core Concepts
02:46:04 LangChain Components
02:52:01 LangChain Case Study
02:52:57 Add LangChain App
02:57:52 LangChain App Execution
03:02:51 Introduction to LLMs
03:13:44 Limitations of LLMs
03:15:51 Introduction to RAG
03:22:11 RAG Basics Concepts and Terminology
03:34:23 Key Components – Retrieval and Generation
03:42:47 Workflow of RAG
04:17:28 Steps to implement RAG with LangChain
04:26:17 Hands-on RAG – Importing Libraries
04:37:53 Hands-on RAG in detail
04:45:09 Why are LLMs Important
04:45:52 Abstract of the Email Generator App
04:45:52 Generative AI crash course
04:46:04 Software Requirements for App
04:48:50 Implementation of the App
04:58:11 Executing the App
#generativeai #aiproject #ai
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