applied ai engineering samples

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Documentation: https://googlecloudplatform.github.io/applied-ai-engineering-samples/

Source Code: https://github.com/GoogleCloudPlatform/applied-ai-engineering-samples

Welcome to the Google Cloud Applied AI Engineering repository. This repository contains reference guides, blueprints, code samples, and hands-on labs developed by the Google Cloud Applied AI Engineering team.

Applied AI Engineering: Catalog

Generative AI on Vertex AI

This section contains code samples and hands-on labs demonstrating the use of Generative AI models and tools in Vertex AI.

Foundation Models	Evaluation	RAG & Grounding	Agents	Others
Gemini Prompting Recipes Advanced Prompting Foundation model tuning	Vertex GenAI Evaluation Gemini Evals Playbook	Vertex AI Search Retrieval Augmented Generation	Agents Vertex AI Extensions	Developer Productivity with GenAI

Google Cloud AI/ML infrastructure

This section has reference guides and blueprints that compile best practices, and prescriptive guidance for running large-scale AI/ML workloads on Google Cloud AI/ML infrastructure.

Research Operationalization

This section has code samples demonstrating operationalization of latest research models or frameworks from Google DeepMind and Research teams on Google Cloud including Vertex AI.

Solutions Catalog

In addition to code samples in this repo, you may want to check out the following solutions published by Google Cloud Applied AI Engineering.

Solution	Description
GenAI for Customer Experience Modernization	This solution shows how customers can have modern, engaging interactions with brands, and companies can improve the end user, agent, and customer experiences with a modern customer service platform on Google Cloud.
Creative Studio \| Vertex AI	Creative Studio is a Vertex AI generative media example user experience to highlight the use of Imagen and other generative media APIs on Google Cloud.
RAG Playground	RAG Playground is a platform to experiment with RAG (Retrieval Augmented Generation) techniques. It integrates with LangChain and Vertex AI, allowing you to compare different retrieval methods and/or LLMs on your own datasets. This helps you build, refine, and evaluate RAG-based applications.