GCP200DEVLLM

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.

✓ Formation officielle Google CloudNiveau Intermediate⏱️ 1 jour (7h)

Ce que vous allez apprendre

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.

Prérequis

Completion of "Introduction to Developer Efficiency on Google Cloud" or equivalent knowledge.

Public cible

Customers

Programme de la Formation

5 modules pour maîtriser les fondamentaux

Objectifs

Explore the different options available for using generative AI on Google Cloud.

Sujets abordés

→Vertex AI on Google Cloud
→Generative AI options on Google Cloud
→Introduction to course use case

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

Teaching Methods Used

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).

Evaluation and Monitoring System

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.