Google Gemini for Developers (2024)

Course details

Google Gemini is a family of multimodal large language models that works seamlessly across image, video, audio, and code. In this course, instructor Lynn Langit shows you how to use Google Gemini design patterns, tools, and best practices for building LLM-based applications. These include patterns for requirements identification and design, which can include prompt engineering, RAG patterns, and fine-tuning. Learn about the capabilities of Google Gemini, starting with understanding the generative AI studio, and then diving into the development, evaluation, and deployment of models. And explore Gemini applications like the Vertex AI model garden and the Vertex AI agent builder.

Concepts

0. Introduction

01 - Build LLM-based applications with Google Gemini
02 - What you should know
03 - Using cloud services

1. Gemini Dev Environments

04 - Understanding Google Gemini
05 - Use Google AI Studio
06 - Use Vertex AI Studio
07 - Use Colab Notebooks
08 - Use Gemini Code Assist in cloud workstations

2. Gemini Prompts

09 - Use Google AI Studio to test prompts
10 - Use system instructions with prompts
11 - Design and test language model prompts
12 - Design and test multimodal prompts
13 - Design prompts in Cloud Code for APIs

3. Gemini Notebooks and APIs

14 - Using the Gemini API - Set up
15 - Using the Gemini API - Testing prompts
16 - Using function calling with Gemini
17 - Programming multimodal use cases
18 - Use the Gemini File API
19 - Use embeddings with Gemini
20 - Set up a RAG pattern with Gemini
21 - Implement a RAG pattern with Gemini

4. Gemini Model Evaluation

22 - Understand model grounding
23 - Ground a model with Google Search
24 - Ground with a semantic retriever
25 - Understand model evaluation
26 - Perform model evaluation
27 - Fine-tune a Gemini model

5. Gemini Applications

28 - Use Vertex AI Model Garden
29 - Deploy a GenAI cloud architecture - Document summaries
30 - Deploy a GenAI cloud architecture - Knowledge base
31 - Preview of Vertex AI Agent Builder

Conclusion

32 - Next steps

Google Gemini for Developers (2024)

Authors

Lynn Langit