Generative AI From Beginner to Paid Professional, Part 2: Master Prompt Design, Gemini Multimodal in Vertex AI Studio, LangChain, Launching & Deploying Generative AI Projects

0. About The Series

This book will help you unlock the power of generative artificial intelligence and accelerate your journey from beginner to paid professional.

If you’re eager to break into the world of Generative AI but unsure where to start, this step-by-step series is designed to help you quickly grasp the fundamentals of AI and harness its potential to enhance your skills, grow your career, and start monetizing your expertise.

Whether you’re a student, freelancer, or professional looking to boost your expertise, this book series will guide you every step of the way, turning you into a confident AI professional ready to meet the demands of a rapidly evolving market.

1. Introduction

1.1. Learning Objectives

Welcome to Part 2 of our training series! In this section, we dive into Vertex AI Studio, a powerful tool for prototyping and customizing generative AI models. Through engaging lessons and a hands-on lab, you’ll walk through the entire generative AI workflow, discovering how to harness Vertex AI Studio for Gemini multimodal applications, effective prompt design, and model tuning. The goal is to help you unlock the full potential of these tools in your projects with Vertex AI Studio.

Next, we'll explore LangChain, a versatile framework for creating complex AI applications with language models. From foundational concepts like Components, Chains and Agents to practical applications like customer service automation and content generation, this guide gives you the skills to use LangChain effectively.

Each chapter will teach you core techniques, from setting up custom integrations to optimizing applications for real-world deployment. Covering both basic principles and advanced strategies, this book is a practical resource for developers and AI enthusiasts eager to harness LangChain’s potential across various fields.

A Summary of What You’ll Learn:

Understand Vertex AI Studio’s capabilities and its role in generative AI.

Integrate Gemini multimodal with Vertex AI Studio for diverse applications.

Design effective prompts by applying best practices and custom configurations.

Tune models by exploring various methods to find what works best for your needs.

Gain a practical guide to mastering LangChain, with real-world applications to build and deploy powerful AI solutions.

2. Generative AI Workflow

2.1. Introduction to the Vertex AI Studio

Vertex AI Studio is a powerful tool for cloud developers looking to tap into Google’s advanced generative AI models. With Vertex AI Studio, you can test, fine-tune, enhance, and deploy these models to build applications driven by generative AI. This book will give you the essential knowledge and skills to work with Vertex AI Studio. We’ll start by guiding you through the generative AI workflow and introducing key features of the platform, such as Gemini multimodal capabilities, prompt design, and model tuning.

To conclude, we’ll guide you through a hands-on practice that allows you to apply everything you’ve learned in the book, solidifying your skills for real-world scenarios.

2.2. Generative AI

Generative AI is changing the way we interact with technology in exciting ways. But what exactly is it? In simple terms, generative AI is a type of artificial intelligence that creates content for you. This content can take many forms: text, images, audio, and even video.

Given a prompt or request, generative AI can assist with a range of tasks, from summarizing documents and extracting information to generating code, crafting marketing campaigns, creating virtual assistants, and building call center bots, just to name a few.

How does AI generate new content? It learns from a vast amount of existing material, including text, images, and videos, in a process called training. This training creates what’s known as a foundation model. These models are large, complex systems with millions (or even billions) of parameters, requiring extensive data and powerful computing resources to develop.

2.3. Foundation Models

A large language model (LLM) like PaLM, short for Pathways Language Model, is a prime example of a foundation model. Apart from PaLM, Google has trained several other foundation models, including

Pathways Language Model (https://ai.google/discover/palm2)

Gemini (https://gemini.google.com) for multimodal processing,

Codey (https://codey.online/en) for code generation, and

Imagen (https://deepmind.google/technologies/imagen-3) for image processing.

As these models evolve, this list may change, especially as Gemini has the capability to process data across multiple modalities, potentially consolidating some of these models’ functions.

A pre-trained foundation model can generate content and tackle broad tasks like content extraction and document summarization. However, it can also be further trained or fine-tuned with specialized datasets in your field to address specific needs, such as financial model generation or healthcare consulting. This fine-tuning results in a new, customized model that is optimized for your unique applications.

Figure 2.1: How foundation models work

2.3.1. How Foundation Models Power Your Applications

Wondering how to harness a foundation model to power your applications or how to fine-tune it for a specific field? Google Cloud’s Vertex AI is can help. Vertex AI is a complete machine learning platform designed to support the entire ML lifecycle—from model creation to deployment and management.

With Vertex AI, you have access to two main capabilities: Predictive AI and Generative AI. Predictive AI enables you to build models for tasks like forecasting, while Generative AI allows you to use and customize generative models to create content. See Figure 2.2.

Figure 2.2: Capabilities of the Vertex AI

But how do you actually get started with generative AI models in Vertex AI? Let’s walk through the workflow together in Figure 2.3.

Figure 2.3: GenAI workflow on Vertex AI

Input Prompt: Start by entering a prompt, a natural language request, to the generative AI models via the Vertex AI Studio interface.

Responsible AI and Safety Measures: The prompt then goes through responsible AI and safety checks, which you can configure through the interface or via code.

Foundation Models: Once screened, the prompt is sent to foundation models like Gemini multimodal, or other generative models such as Imagen and Codey, depending on your selection.

Model Customization: If needed, you can further tune these generative AI models to better align with your specific data and use cases.

Results Grounding: The models return results that undergo grounding and citation checks to minimize inaccuracies or hallucinations.

Final Response: After a final pass through responsible AI and safety filters, the response is displayed on the Vertex AI Studio interface.

Vertex AI Studio offers an intuitive, user-friendly interface that lets you build generative AI applications in a low-code or even no-code setting. This means you can quickly test and prototype models, tune and customize them with your own data, enhance them with real-world, up-to-date information, and efficiently deploy them in production environments, complete with auto-generated code to streamline the process. See Figure 2.4.

Figure 2.4: What Vertex AI Studio Offers

Vertex AI Studio supports a wide range of tasks across multimodal, language, vision, and speech domains, and as you move through this guide, that range may continue to grow.

With its multimodal capabilities, Vertex AI Studio can process data across different formats, like images, videos, and text, allowing for tasks such as extracting text from an image.

For language tasks, you can craft prompts to perform specific functions and fine-tune language models.

In vision, you can generate and edit images based on prompts.

For speech, the platform enables you to convert speech to text and vice versa. In the next few chapters, you’ll dive deeper into the possibilities and applications of multimodal and language functionalities.

3. The Gemini Multimodal

Let’s start with the Gemini multimodal, Google’s most versatile and advanced model to date. But what exactly is a multimodal model? It’s a large foundation model designed to handle different types of input, like text, images, and video, and even generate content across these formats.

For example, you could upload a photo of a plate of cookies and ask for a recipe based on what it sees. Gemini, Google’s multimodal model available on Vertex AI Studio, can process text, image, and video inputs. At the moment, its output is limited to text, though this may expand as you move further in the series.

3.1. Gemini Business Use Cases

How can Gemini support your business needs? Gemini is a powerful tool that brings flexibility to a wide range of tasks by working seamlessly with text, images, and videos. Here are some practical ways it can be valuable for your business:

Description and Captioning

Gemini can identify and describe objects in images or videos, tailoring its responses to be as detailed or as brief as you need. This can be useful for labeling content or enhancing accessibility.

Information Extraction

Need to pull specific details from images or videos? Gemini can read and extract key information, whether it's text from a document, labels from packaging, or other essential data for further processing.

Information Analysis

Gemini can go a step further by analyzing the extracted information based on your requirements. For example, it can categorize expenses on a receipt or flag key points in documents, saving time and improving accuracy.

Information Seeking

Gemini can answer questions or generate Q&A content based on the data it extracts from images or videos, turning complex information into easily understandable responses.

Content Creation

Gemini can even use images and videos as inspiration to create engaging stories or advertisements, helping you expand your content reach and connect with your audience creatively.

Data Conversion

Gemini makes data versatile. It can transform its responses into various formats like HTML or JSON, making it easy to integrate into your systems or use across platforms.

Whether you need help with content management, data analysis, or interactive user engagement, Gemini’s multimodal capabilities can simplify complex tasks and enhance your workflow.

Try to imagine a real-world scenario where Gemini’s multimodal abilities would make a difference. With these exciting new features, developers have the chance to build applications that fully harness Gemini’s unique strengths.

3.2. How to Interact with Gemini Multimodal

How can developers start creating with Gemini to bring multimodal technology into practical, everyday use? There are three primary approaches, each essentially achieving the same objective.

Using a user interface (UI) with the Google Cloud console: This no-code solution is ideal for exploring and testing prompts.

Using predefined SDKs with notebooks like Colab and Workbench, which are seamlessly integrated within the Vertex AI platform.

Utilizing Gemini APIs in conjunction with command-line tools like CURL.

Regardless of which method to access the Gemini multimodal, you start with a prompt. So, what is a prompt?

3.2.1. Starting with a Prompt

In the world of Generative AI, a prompt is a natural language request submitted to a model in order to receive a response. When using the model, you start by giving it your desired input: questions, tasks, or instructions. The responses you get will depend on how you frame your prompts, so asking clear, well-structured questions is key.

3.2.2. Anatomy of a Prompt

A prompt usually consists of these main components: input, context, and examples.

Input is your main request for a response. It can be:

A question (e.g., What should I do when my computer freezes?)

A task you’d like the model to perform

An entity the model should act upon

A Completion input (partial input) that the model can complete or continue

Context adds guidance to your prompt. It can include instructions for how the model should respond or extra details for reference. For example, if you want the model to play the role of an IT help desk, you might specify that it should always suggest restarting the computer, regardless of the issue.

Examples help the model understand the response format you want. By giving example pairs of questions and answers, you can guide the model’s style. For instance:

Lost internet connection: reset.

Network printer issue: restart.

With this setup, if you then ask, What should I do when my computer runs slowly? the model will likely suggest a reset, following the pattern you provided.

In short, structuring your prompts thoughtfully with these elements can significantly improve the quality of responses.

Context and examples play a crucial role in training or fine-tuning Generative AI models to respond just the way you want.

3.3. Language Capabilities: Prompt Design Modes

Vertex AI Studio provides two main prompt design modes to help you create responses effectively:

1. Free-Form Mode

Flexibility: This mode allows you to enter any text, instruction, or question directly, offering maximum freedom.

Creative Applications: Perfect for tasks like writing stories, poems, or generating code.

Experimentation: You can try different wordings and styles to fine-tune the output.

2. Structured Mode

Guided Prompts: This mode provides templates for specific tasks like summarization, translation, or Q&A.

Consistent Results: Using templates helps keep responses consistent and predictable.

Simplified Workflow: Especially useful for beginners or for tasks that require a straightforward approach.

Choosing the Right Mode

Your choice depends on your needs:

Free-Form Mode: Best for creative tasks, exploratory work, or when you have a clear vision for the output.

Structured Mode: Ideal for specific, defined tasks or when you want a quicker, more streamlined approach.

Understanding these modes allows you to make the most of Vertex AI Studio and tailor outputs to fit your goals.

3.3.1. Free-form Prompt Design: Hands-on Practice 1

Crafting the ideal input text to get the response you