Google Cloud Generative AI – how to enhance app development?

Generative AI applications are revolutionizing the landscape of app development, offering unparalleled capabilities harnessing the power of natural language processing and deep learning algorithms to create content ranging from text to images, music, and videos. Google Cloud Generative AI models will help you reduce the development cost to a fraction.

Given the ready availability of multiple gen AI models, the real challenge companies face now is how to tweak them to their needs to get the most out of the technology.

Gen AI use cases

Let’s consider the following use cases:

• Reading customer emails and converting them into tickets with all the proper context and necessary documentation
• Retrieving specifics from contracts and running preliminary analysis
• Drafting client correspondence based on the communication up to this point
• Analysing extensive reports and providing a concise summary, different for different teams/departments/management levels.

All of these tasks can be easily automated with generative AI.

AI savings

Using existing, powerful generative AI models creates an opportunity unlike anything you had at your disposal before. You can develop and deploy secure, scalable AI-driven solutions without incurring the cost of developing a dedicated app. Remember that working with a trusted AI partner guarantees both security and efficacy of deployed solutions.

Develop apps with Gen AI

Harnessing the full potential of generative AI requires a comprehensive understanding of its intricacies and challenges at each stage of development. We delve into the key aspects of developing a generative AI application on Google Cloud, offering insights and best practices to streamline your development process.

Assessing technical readiness

Before embarking on your generative AI journey, it’s essential to evaluate your organization’s technical readiness. Assess your AI capabilities and infrastructure to ensure they align with the requirements of generative AI development.

Choosing the Right Model

Selecting the appropriate model is crucial for the success of your generative AI application. The closer the model is to your needs, the less uptraining it will require.

Factors to consider when choosing a model:

• Modality
• Size
• Cost, and
• Features.

Google Cloud offers a diverse range of generative AI models through Vertex AI and Model Garden, catering to various use cases and requirements. Choose a model that strikes the right balance between affordability, response quality, and latency to meet your application’s needs effectively.

Customizing the Model

While some models may perform well out of the box, customization may be necessary to address specific challenges or improve performance. Techniques such as function calling, extensions, grounding, and model tuning offer avenues to fine-tune the model’s behavior and enhance its capabilities.

Experiment with customization techniques to optimize your model for your application’s unique requirements.

If you want to learn more about developing an app with Google Cloud generative AI models, you can find a guide here.

Prompt design

Prompt design plays a pivotal role in shaping the behavior of generative AI models. Authoring prompt and response pairs provides additional context and instructions to the model, influencing its output. Explore prompt design strategies to guide the model towards generating specific outputs tailored to your requirements.

Evaluating model performance

Model evaluation is essential to assess the effectiveness of your customizations and prompt designs. Employ a combination of metrics-based evaluations and human evaluations to gain comprehensive insights into the model’s performance.

Google Cloud’s automatic side-by-side evaluation feature in Vertex AI facilitates quick comparisons between model outputs and ground truth, enabling rapid iteration and refinement.

Deploying the Model

Deploying your model marks the culmination of the development process, making it available for serving online predictions. Choose between fully managed and self-managed deployment environments based on your requirements for scalability, latency, and budget.

Google Cloud offers versatile deployment options, allowing you to tailor your deployment strategy to suit your application’s needs effectively.

Ensure responsible AI

Despite being more and more ubiquitous, generative AI is still a young technology. As such, it carries risks of exacerbating information hazards and amplifying unfair biases, even creating new cybersecurity risks. It is all the more important to use AI from a provider with sufficient focus on security.

You should also make sure you uphold principles of responsible AI to mitigate potential risks and ensure ethical use. Test your models rigorously to identify and address unintended biases or harmful outputs.

By understanding the limitations of generative AI models and prioritizing responsible development practices, you can leverage the transformative potential of AI while minimizing associated risks.

Get more from Gen AI

Google Cloud’s robust suite of tools and services empowers developers to harness the full potential of generative AI in app development. By following best practices and leveraging the capabilities of Google Cloud’s AI offerings, you can unlock new possibilities and drive innovation in your applications. If you want to embrace the power of generative AI on Google Cloud but you’re not sure how to start, get in touch with our cloud engineers and find out what capabilities Gen AI offers.