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Building AI Apps with the Gemini API
Introduction
Introduction (3:25)
FAQ
Exercise: Meet Your Classmates and Instructor
Course Resources
Setting Up the Environment
What We're Using (0:35)
Jupyter Notebook (14:10)
Google Colab (8:07)
Deep Dive into Google Gemini Pro API
Getting a Gemini API Key (4:20)
Installing the Python SDK for Gemini API and Authenticating to Gemini (9:51)
Gemini Multimodal Models: Nano, Pro, and Ultra (5:14)
Google AI Studio: Freeform Prompts With Gemini Pro Vision (6:11)
Google AI Studio: Using Variables and Parameters in the Prompt (3:04)
Generating Text From Text Inputs: Gemini Pro (4:22)
Streaming Model Responses (3:35)
Generating Text From Image and Text Inputs: Gemini Pro Vision (5:12)
Gemini API Generation Parameters: Controlling How the Model Generates Responses (6:11)
Gemini API Generation Parameters Explained (10:13)
Building Chat Conversations (7:53)
Project: Building a Conversational Agent Using Gemini Pro (7:18)
Project: Talking With an Image
Project Requirements (5:53)
Building the Application (5:22)
Testing the Application (1:48)
Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps (2:48)
Creating the Web App Layout With Streamlit (11:19)
Saving and Displaying the History Using the Streamlit Session State (5:19)
Project: Building an AI-Powered Image Renaming Tool
Project Introduction (0:56)
Getting Images Using a Generator (6:17)
Renaming Images Using Gemini Pro Vision (9:34)
Prompt Engineering for Gemini API
Intro to Prompt Engineering the Gemini API (3:12)
Tactic #1 - Position Instructions Clearly With Delimiters (5:01)
Tactic #2 - Provide Detailed Instructions for the Context, Outcome, or Length (6:10)
Tactic #3 - Specify the Response Format (6:13)
Tactic #4 - Few-Shot Prompting (6:55)
Tactic #5 - Specify the Steps Required to Complete a Task (6:28)
Tactic #6 - Give Models Time to "Think" (4:33)
Other Tactics for Better Prompting and Avoiding Hallucinations (6:20)
Prompt Engineering Summary (2:12)
Where To Go From Here?
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Gemini API Generation Parameters: Controlling How the Model Generates Responses
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