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Google Gemini for Developers

via LinkedIn Learning

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Overview

Learn best practices, patterns and processes for developers and DevOps teams who design and implement LLM-based applications using Google Gemini.

Syllabus

Introduction

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

1. Gemini Dev Environments

Understanding Google Gemini
Use Google AI Studio
Use Vertex AI Studio
Use Colab Notebooks
Use Gemini Code Assist in cloud workstations

2. Gemini Prompts

Use Google AI Studio to test prompts
Use system instructions with prompts
Design and test language model prompts
Design and test multimodal prompts
Design prompts in Cloud Code for APIs

3. Gemini Notebooks and APIs

Using the Gemini API: Set up
Using the Gemini API: Testing prompts
Using function calling with Gemini
Programming multimodal use cases
Use the Gemini File API
Use embeddings with Gemini
Set up a RAG pattern with Gemini
Implement a RAG pattern with Gemini

4. Gemini Model Evaluation

Understand model grounding
Ground a model with Google Search
Ground with a semantic retriever
Understand model evaluation
Perform model evaluation
Fine-tune a Gemini model

5. Gemini Applications

Use Vertex AI Model Garden
Deploy a GenAI cloud architecture: Document summaries
Deploy a GenAI cloud architecture: Knowledge base
Preview of Vertex AI Agent Builder

Conclusion

Next steps

Taught by

Lynn Langit

Reviews

4.7 rating at LinkedIn Learning based on 57 ratings

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