The Era of Substantial AI: A Generative AI Cookbook

The transformative power of Generative AI (GenAI) in Google Cloud Platform (GCP) is akin to a well-stocked kitchen for the modern developer. With the right ingredients and recipes, one can concoct previously unimaginable solutions.

Hi, this is Madhu Vadlamani — Data Analytics and AI expertise, Speaker and mentor and in this article serves as a cookbook, guiding you through the creation of robust GenAI applications, with a focus on the role of DUE and RAG applications in GCP.

Generative AI: The Ingredients of Innovation
Generative AI in GCP offers a plethora of tools that act as the primary ingredients for innovation

Vertex AI: The cornerstone of GenAI in GCP, providing a comprehensive environment for building, deploying, and scaling AI models.

Gemini Models: Google’s foundation models that offer versatility in tasks such as reasoning, code generation, and more.

Crafting Recipes with Generative AI

Like any good cookbook, GCP provides recipes that detail the process of creating GenAI applications:

Setting Up Your Kitchen (Environment):
— Begin by exploring the [Generative AI repository on GitHub](¹^), which is like your pantry filled with code samples and notebooks.
— Set up your GCP environment by following the (setup instructions)* provided in the repository.

Selecting Your Ingredients — Models and Tools:
— Choose from a variety of models in the Model Garden.
— Utilize prompt design and management tools for crafting effective AI interactions.

Cooking the Dish (Developing Applications):
— Follow the recipes for model customization to tailor the AI’s output to your needs.
— Integrate real-time data using Vertex AI Extensions for up-to-date and accurate responses.

The Role of DUE and RAG in the GenAI Kitchen

DUE and RAG applications are like specialized kitchen gadgets that enhance the cooking experience:

DUE (Data Usage Effectiveness): Ensures that the data used by AI models is effective, relevant, and secure. It’s about optimizing the data for better performance and results.

RAG (Retrieval Augmented Generation): Acts as a sous-chef, assisting the main AI model by retrieving external information to augment its responses. This technique is crucial for applications requiring up-to-date or domain-specific information.

RAG works above LLM: The LLM capability is limited to a point which is retrival centric but not cannot involove in completedecision making.

Implementing RAG in GCP:
1. Integrating with Operational Databases:
— Use RAG to connect your AI models with databases like AlloyDB or Cloud SQL, allowing them to pull in real-time data for more accurate responses.

2. Streamlining Workflows with LangChain:
— LangChain is an orchestration framework that simplifies the creation of RAG applications. It allows for the integration of document loaders, vector stores, and memory components to build complex workflows.
I personally like the idea behind LangChain which is one of the most optimized yet curative model in the era of GEN AI

3. Building Custom RAG Applications:
— Leverage the quickstart solution and reference architecture for RAG applications to deploy on Google Kubernetes Engine (GKE) and Cloud SQL.

Finally
GEMINI CODE Integration: Don’t forget to use Gemini for expert assistance across your entire software development lifecycle, including application modernization and code refactoring