Application Development with LLMs on Google Cloud
In this course, you explore tools and APIs available on Google Cloud for integrating large language models (LLMs) into your application. After exploring generative AI options on Google Cloud, you explore LLMs and prompt design in Vertex AI Studio. Then you learn about LangChain, an open-source framework for developing applications powered by language models. After a discussion around more advanced prompt engineering techniques, you put it all together to build a multi-turn chat application by using LangChain and the Vertex AI Gemini API.
What you will learn
- Explore the different options available for using generative AI on Google Cloud.
- Use Vertex AI Studio to test prompts for large language models.
- Develop LLM-powered applications using LangChain and LLM models on Vertex AI.
- Apply prompt engineering techniques to improve the output from LLMs.
- Build a multi-turn chat application using the Gemini API and LangChain.
Prerequisites
- Completion of "Introduction to Developer Efficiency on Google Cloud" or equivalent knowledge.
Target audience
- Customers
Training Program
5 modules to master the fundamentals
Objectives
- Explore the different options available for using generative AI on Google Cloud.
Topics covered
- →Vertex AI on Google Cloud
- →Generative AI options on Google Cloud
- →Introduction to course use case
Objectives
- Use Vertex AI Studio to test prompts for large language models.
- Understand how Vertex AI Studio keeps your data secure.
Topics covered
- →Introduction to Vertex AI Studio
- →Available models and use cases
- →Designing and testing prompts in the Google Cloud console
- →Data governance in Vertex AI Studio
Activities
Lab: Exploring Vertex AI Studio
Objectives
- Understand basic concepts and components of LangChain.
- Develop LLM-powered applications using LangChain and LLM models on Vertex AI.
Topics covered
- →Introduction to LangChain
- →LangChain concepts and components
- →Integrating the Vertex AI Gemini APIs
- →Question/answering chain using Gemini API
Activities
Lab: Getting Started with LangChain + Vertex AI Gemini API
Objectives
- Apply prompt engineering techniques to improve the output from LLMs.
- Implement a RAG architecture to ground LLM models.
Topics covered
- →Review of few-shot prompting
- →Chain-of-thought prompting
- →Retrieval augmented generation (RAG)
- →ReAct
Activities
Lab: Prompt Engineering Techniques
Objectives
- Understand the concept of memory for multi-turn chat applications.
- Build a multi-turn chat application by using the Gemini API and LangChain.
Topics covered
- →LangChain for chatbots
- →Memory for multi-turn chat
- →Chat retrieval
Activities
Lab: Implementing RAG Using LangChain
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote