Discover the evolution, architecture, and APIs of Google Gemini. Delve into hands-on exercises, mastering RAG applications, and develop a customer service assistant using multimodal prompting with image and text prompts.

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textretrieval

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This course will introduce you to Google Gemini, a family of multimodal large language models developed by Google.

You’ll start with learning about LLMs, the evolution of Google Gemini, its architecture and APIs, and its diverse capabilities. Next, you’ll complete hands-on exercises using Gemini models for unimodal and multimodal text generation. You’ll understand the retrieval augmented-generation (RAG) process using Gemini and LangChain. You’ll implement an RAG application for generating textual responses based on the provided unimodal prompts and an external knowledge source. Finally, you’ll develop a customer service assistant application with a Streamlit interface that integrates RAG and Gemini for multimodal prompting using image and text prompts.

After completing this course, you will have an in-depth knowledge of using Google Gemini for unimodal and multimodal prompting in real-world AI-based applications.

Building Multimodal RAG Applications with Google Gemini

It focuses only on generating different text formats.

It can process and understand data in the form of both text and images. 

It is only designed for sentiment analysis and emotion detection.

It is pretrained on a specific domain before use.


How does Google Gemini offer more flexibility compared to unimodal LLMs?

How does Google Gemini offer more flexibility compared to unimodal LLMs?

Use text-to-text generation to write about the image that you described through text.

Use image-to-text generation to show the details of the image.

Manually jot down the details without using any Gemini model.

Use image-to-text generation to write down the details of the cafe item and write a creative caption using text-to-text generation.

Which capability of Google Gemini should be used to generate a social media marketing post for a newly opened coffee bar in town?

Which capability of Google Gemini should be used to generate a social media marketing post for a newly opened coffee bar in town?

Which variant of Gemini 1.5 is optimized for speed and efficiency, performing at a similar level to the 1.0 Ultra model on some benchmarks?

Which variant of Gemini 1.5 is optimized for speed and efficiency, performing at a similar level to the 1.0 Ultra model on some benchmarks?

`response = model.generate_content([
  “Summarize the following news article.”,
  “article.txt”])`

`response = model.generate_content([“Summarize the key points of the following news article in 2-3 sentences.”, “article.txt”])`

`response = model.generate_content([“Provide a detailed analysis of the news article”, “article.txt”])`

`response = model.generate_content([“Write a short story based on this news article.”, “article.txt”])`

Which prompt is the most efficient for generating a brief summary of a text file `article.txt` containing a news article?

Which prompt is the most efficient for generating a brief summary of a text file <code>article.txt</code> containing a news article?

Which Gemini model variant would be most suitable for deploying a machine learning model that processes large datasets efficiently on a cloud platform?



Which Gemini model variant would be most suitable for deploying a machine learning model that processes large datasets efficiently on a cloud platform?

Getting Started

Content Generation Using Gemini Models

Building RAG Applications with Google Gemini

Customer Service Assistant—Multimodal RAG Interface

Wrapping Up

Quiz: Basics of Google Gemini