Foundation models—such as the large language models (LLMs) used to generate content and power AI agents—are powerful tools that enable enterprises to drive efficiency gains and launch innovative offerings.

Many businesses have prioritized pilot deployments of gen AI, using models to create new content, translate languages, produce different text formats, and answer your customer and employee questions.

We’re inspired by what enterprises like yours have been building. We’ve seen a staggering 36x increase in Gemini API usage and nearly 5x increase of Imagen API usage on Vertex AI this year alone—demonstrating that enterprises are moving from gen AI experimentation to real-world production.

But extracting value from gen AI for your enterprise isn’t as simple as typing a query into a model and getting a response. Taking full advantage of gen AI’s capabilities requires a comprehensive strategy, including model selection, prompt management, evaluation, integrating retrieval augmented generation (RAG), and more.

It can feel overwhelming. But it doesn’t have to be. This guide shares critical learnings from how our customers have moved from AI experimentation to production, and will help you get started