Course Overview of Application Development with LLMs on Google Cloud

This instructor-led course introduces learners to application development using Large Language Models (LLMs) on Google Cloud. Participants will explore the foundational concepts of Generative AI, understand Google’s Gemini family of models, and learn how to design intelligent applications powered by LLMs. 

The course covers prompt engineering techniques, grounding concepts, LangChain integration, Retrieval-Augmented Generation (RAG), and chatbot development using Vertex AI Gemini APIs. Learners will also gain hands-on experience using Vertex AI Studio for prompt testing, experimentation, and application prototyping. 

Through guided labs and real-world implementation scenarios, participants will develop practical skills for building conversational AI applications, integrating Gemini APIs, and creating advanced prompt-driven workflows on Google Cloud.

After completing Application Development with LLMs on Google Cloud, participants will be able to

  • Understand Generative AI and Large Language Model concepts
  • Explore Generative AI options available on Google Cloud
  • Use Vertex AI Studio to design and test prompts
  • Build applications using Gemini models and APIs
  • Apply advanced prompt engineering techniques
  • Implement grounding and Retrieval-Augmented Generation (RAG)
  • Integrate LangChain with Vertex AI Gemini APIs
  • Build multi-turn conversational AI applications
  • Create chatbot applications using Gemini APIs
  • Improve LLM outputs using prompt architectures and reasoning techniques

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Key Features of Application Development with LLMs on Google Cloud

  • Comprehensive LLM Application Development Training

  • Hands-On Learning Experience

  • Vertex AI Studio Exploration

  • Advanced Prompt Engineering Techniques 

  • Grounding and RAG Implementation

  • LangChain and Gemini API Integration

  • Google Cloud AI Ecosystem Exposure

  • Modern Conversational AI Best Practices

Who should Attend Application Development with LLMs on Google Cloud?

  • Application Developers
  • AI Developers
  • Generative AI Engineers
  • Software Engineers
  • Cloud Engineers
  • Conversational AI Developers
  • Machine Learning Engineers
  • Solution Architects
  • Professionals building AI-powered applications

Prerequisites of Application Development with LLMs on Google Cloud

  • Completion of “Introduction to Developer Efficiency on Google Cloud” or equivalent knowledge
  • Basic programming knowledge in Python
  • Familiarity with APIs and application development concepts
  • Understanding of cloud fundamentals is recommended
  • Interest in Generative AI and conversational AI development

    • Why choose CloudThat as your training partner for Application Development with LLMs on Google Cloud ?

    • Specialized Google Cloud AI Expertise - CloudThat specializes in cloud and Generative AI technologies, delivering industry-focused Google Cloud AI training programs with practical enterprise implementation experience.
    • Industry-Recognized Trainers - Our trainers are certified Google Cloud professionals with expertise in Vertex AI, Gemini models, prompt engineering, LangChain, and conversational AI development.
    • Hands-On Learning Approach - CloudThat emphasizes practical learning through guided labs, real-world AI implementation exercises, chatbot development scenarios, and prompt engineering workflows.
    • Customized Learning Paths - Training programs are designed for developers, AI engineers, architects, and cloud professionals with varying levels of AI and application development expertise.
    • Interactive and Practical Sessions - Sessions include live demonstrations, architecture discussions, implementation walkthroughs, troubleshooting exercises, and collaborative AI activities.
    • Career and Certification Support - CloudThat supports learners with AI project guidance, interview preparation, and practical enterprise Generative AI implementation strategies. 
    • Updated Industry-Relevant Content - Course content is continuously updated to align with the latest advancements in Gemini models, Vertex AI, prompt engineering, RAG systems, and conversational AI technologies. 
    • Trusted by Enterprises Worldwide - Thousands of professionals and organizations trust CloudThat for advanced cloud, AI, and Generative AI training programs. 

    Learning objectives of Application Development with LLMs on Google Cloud

    • Understand Generative AI and Large Language Model concepts
    • Explore Google Cloud Generative AI ecosystem and Gemini models
    • Design and test prompts using Vertex AI Studio
    • Apply advanced prompt engineering techniques
    • Integrate Gemini APIs into applications
    • Build conversational AI and chatbot applications
    • Implement grounding and Retrieval-Augmented Generation (RAG) workflows
    • Utilize LangChain for multi-turn conversational systems
    • Improve LLM reliability, reasoning, and contextual understanding
    • Build enterprise-ready AI-powered applications on Google Cloud

    Course Outline of Application Development with LLMs on Google Cloud Download Course Outline

    Lecture Content

    • What is Generative AI
    • Vertex AI on Google Cloud
    • Generative AI Options on Google Cloud
    • Introduction to Course Use Case
    • Understanding LLM Application Architectures

    Learning Objectives

    • Understand Generative AI concepts and LLM fundamentals
    • Explore Vertex AI as a Generative AI platform
    • Identify different Generative AI options available on Google Cloud

    Lab Content

    • NA

    Lecture Content

    • Introduction to Vertex AI Studio
    • Designing and Testing Prompts
    • Prompt Experimentation Workflows
    • Data Governance in Vertex AI Studio
    • Prompt Prototyping Best Practices

    Learning Objectives

    • Use Vertex AI Studio for prompt design and testing
    • Experiment with prompts for Gemini models
    • Understand governance concepts in Vertex AI Studio

    Lab Content

    • Lab: Getting Started with the Vertex AI Studio User Interface

    Lecture Content

    • Introduction to Grounding
    • Integrating Vertex AI Gemini APIs
    • Chat, Memory, and Grounding Concepts
    • Search Principles for LLM Application
    • Conversational AI Architecture Concepts

    Learning Objectives

    • Understand grounding concepts for LLMs
    • Integrate Vertex AI Gemini APIs into applications
    • Build contextual and memory-aware chat workflows
    • Apply search principles to AI applications

    Lab Content

    • Lab: Getting Started with LangChain + Vertex AI Gemini API

    Lecture Content

    • Review of Few-Shot Prompting
    • Chain-of-Thought Prompting and Thinking Budgets
    • Meta Prompting Techniques
    • Multi-Step and Panel Prompts
    • Retrieval-Augmented Generation (RAG)
    • React Prompting Strategies

    Learning Objectives

    • Apply advanced prompt engineering techniques
    • Improve reasoning and response quality in LLMs
    • Implement RAG workflows for grounded AI responses
    • Use structured prompting strategies for complex tasks

    Lab Content

    • Lab: Advanced Prompt Architectures

    Lecture Content

    • LangChain for Chatbots
    • ADK for Chatbots
    • Chat Retrieval Concepts
    • Building Multi-Turn Conversations
    • Conversational AI Design Best Practices

    Learning Objectives

    • Build chatbot applications using LangChain and Gemini APIs
    • Create conversational memory workflows
    • Implement chat retrieval mechanisms
    • Design scalable conversational AI systems

    Lab Content

    • Lab: Implementing RAG Using LangChain

    Certification Details of Application Development with LLMs on Google Cloud

      CloudThat Course Completion Certificate.

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    Course ID: 28714

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