Traditional software development was deterministic: if this, then that. You wrote the logic, and the machine followed it. Generative AI has introduced a probabilistic paradigm: here is the context, generate the answer.
This shift from explicit coding to “steering” Large Language Models (LLMs) allows developers to build applications that can reason, summarize, and create. However, it also introduces new challenges in reliability, cost, and latency.
Application Development with LLMs on Google Cloud has rapidly consolidated its AI offerings under Vertex AI, providing a robust ecosystem not just for playing with prompts, but for engineering enterprise-grade applications. If you are ready to move from a “cool demo” to a production application, here is your guide to development on Google Cloud.
The Landscape: Vertex AI and the Model Garden
The heart of Application Development with LLMs on Google Cloud Course is Vertex AI. Unlike scattered API endpoints, Vertex AI provides a unified platform for managing the entire lifecycle of an AI model.
Your journey begins in the Model Garden. This is a curated library where you can access:
First-Party Models: Google’s flagship Gemini models (Pro, Ultra, and Flash) and the open-weights Gemma models.
Third-Party Models: Popular open-source models like Llama (Meta) and Mistral, which can be deployed on Vertex AI infrastructure.
Key Decision: Do you use a managed API (Gemini) or host your own (Llama)?
Start with Gemini 1.5 Pro or Flash. For 90% of use cases, the managed API removes the headache of infrastructure management. Use Flash for high-volume, low-latency tasks, and Pro for complex reasoning.
Phase 1: Prototype — The Art of Prompt Engineering
Before writing a single line of Python code, your development environment is Vertex AI Studio. This implies a “low-code” approach to testing feasibility.
Multimodal Capabilities
One of Google Cloud’s distinct advantages is that Gemini is natively multimodal. You aren’t limited to text-in/text-out. You can feed the model video clips, codebases, or PDFs directly in the prompt window to test how it analyzes them.
Prompt Design vs. Prompt Tuning
Prompt Design: crafting the instructions (system prompts) to guide the model.
Prompt Tuning: If standard prompts fail, you don’t necessarily need to fine-tune the entire model. Vertex AI allows for “Prompt Tuning,” a parameter-efficient method where you train a small adapter layer on your specific data, keeping the frozen base model intact. This is cheaper and faster than full fine-tuning.
Phase 2: Grounding — Solving Hallucinations with RAG
The biggest risk in LLM apps is hallucination — the model confidently making things up. To fix this, you must connect the model to your private business data. This architecture is called Retrieval Augmented Generation (RAG).
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In Google Cloud, this workflow is streamlined through Vertex AI Vector Search (formerly Matching Engine).
The Workflow:
Ingest: You upload your documents (PDFs, Wikis, internal databases) to Cloud Storage.
Embed: Use Vertex AI’s Gecko embeddings model to convert this text into vectors (mathematical representations of meaning).
Index: Store these vectors in Vertex AI Vector Search.
Retrieve: When a user asks a question, the app searches your Vector Index for relevant context first, then sends both the question and the context to Gemini.
Enterprise Search
For developers who don’t want to build the RAG pipeline from scratch, Google offers Vertex AI Search and Conversation. This is a managed “RAG-in-a-box” solution that can index your data and provide an API for grounded answers in minutes.
Phase 3: Production — Orchestration and Evaluation
Writing the prompt is easy. Building the “glue” that holds the app together is the hard part.
Function Calling and Extensions
LLMs are isolated from the world; they cannot check the weather or query a SQL database on their own. Function Calling in Gemini allows you to describe your code’s functions to the model.
Example: You tell Gemini, “I have a function called get_inventory(item_id)." When a user asks, "Do we have shoes in stock?", Gemini outputs a structured JSON object requesting that function be run, rather than just guessing the answer.
Evaluation (The “Unit Test” of AI)
How do you know if your bot is getting better or worse? You cannot rely on “vibes.”
Action: Use Vertex AI Evaluation. This service allows you to define a “Golden Dataset” of questions and ideal answers. You can then run automated metrics (like BLEU or ROUGE scores) or even use a larger LLM to grade the responses of your smaller LLM based on criteria like “helpfulness” or “safety.”
Summary: The “Crawl, Walk, Run” Approach
To avoid getting overwhelmed, adopt a phased strategy:
Crawl: Use Vertex AI Studio to experiment with Gemini 1.5 Flash. Build a simple chatbot that uses system instructions to adopt a persona. Focus on prompt engineering.
Walk: Implement RAG. Index a set of your company’s PDFs using Vertex AI Vector Search. Build a Python application (using the LangChain on Vertex AI framework) that retrieves context before answering user queries.
Run: Build an Agent. Use Function Calling to let your LLM take actions (like booking a calendar slot or querying BigQuery). Set up an automated evaluation pipeline to test your model’s accuracy before every deployment.
Read more about this here:
https://techcroc.mystrikingly.com/blog/application-development-with-llms-on-google-cloud-building-the-nexthttps://vendorgoogletech.blogspot.com/2025/12/llm-on-google-cloud.html
https://mahrab5.wordpress.com/2025/12/09/application-development-with-llms-on-google-cloud-building-the-next-generation-of-apps/
Building with LLMs on Google Cloud is about assembling the right blocks. With Gemini for intelligence, Vector Search for memory, and Vertex AI for governance, you have the toolkit to build applications that truly transform how your business operates.
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