Example Curriculum
Introduction
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Setting Up the Environment
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Deep Dive into Google Gemini Pro API
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- 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
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Project: Building an AI-Powered Image Renaming Tool
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Prompt Engineering for Gemini API
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- 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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