Generative AI is evolving rapidly, and building high-performing applications demands more than just a strong model—it requires access to fresh data and advanced features. A recent two-part presentation explores how integrating real-time data streams with Google’s latest Vertex Gemini capabilities can elevate generative AI systems. This post summarizes both talks, highlighting how these complementary strategies lead to more accurate, reliable, and scalable AI applications.

Talk 1: Real-Time Data Streams and Accurate SQL Automation

The Challenge: Democratizing Data Access

One of the key hurdles in data-driven organizations is making data accessible to non-technical users. Many employees and even customers struggle with writing complex SQL queries. This challenge limits the potential of self-service analytics and delays decision-making.

The Solution: LLM-Powered SQL Generation

Pascal’s solution focuses on using Large Language Models (LLMs), like Gemini, to generate SQL queries based on natural language inputs. By automating SQL generation, businesses can empower a broader range of users to access and analyze data directly.

Why Data Quality and Freshness Matter

Generative AI’s performance is tightly linked to the quality and timeliness of the data it consumes. Static or outdated datasets can lead to inaccurate outputs and hallucinations. In contrast, real-time data ensures models generate relevant, grounded results.

• Streaming platforms like Confluent make real-time data available as it happens.
• Fresh data enhances LLM accuracy, particularly for dynamic queries like identifying nearby healthcare providers.

Implementing Effective SQL Generation

High-quality prompt engineering is essential. Describing each database field in plain English helps the model understand the schema and make accurate joins or filter decisions.

For example, when asked to generate SQL to locate healthcare providers by region, Gemini succeeded in crafting a complex query, correctly handling geolocation and table relationships, because of clearly defined schema descriptions and real-time data streams.

To improve robustness:

• Use replayable events to compare outputs across prompt iterations.
• Incorporate rigorous prompt testing to eliminate errors.

Architectural Design: Real-Time, Event-Driven

The system architecture is built on microservices and event-driven principles:

• Flink Ai enables real-time LLM calls, such as summarizing data or generating SQL within the stream.
• Topics manage the flow, ensuring seamless integration across services.

Key Benefits

• Accessibility: Enables users without SQL skills to interact with data confidently.
• Reliability: Reduces hallucination by using live, accurate data.
• Efficiency: Scalable through managed services like Flink, reducing operational overhead.
• Customer Impact: Enhances the end-user experience through faster and more accurate data responses.

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Talk 2: Unlocking Advanced Gemini Capabilities

Introducing Vertex Gemini 2.0 Flash

Google’s latest iteration, Vertex Gemini 2.0 Flash, offers a rich set of features tailored for developers building advanced AI applications. From structured output to multimodal processing and real-time integrations, Gemini pushes the boundaries of what’s possible in AI development.

Unified SDK: Simplifying Development

With the new Google GenAI SDK, developers can seamlessly build applications in either AI Studio or Vertex AI using a unified codebase. This flexibility enables teams to experiment quickly in AI Studio for rapid prototyping and transition to secure, scalable deployments within the cloud using Vertex AI, depending on their project requirements.

Core Generation Features

Gemini supports a suite of core generation capabilities that streamline development. It offers standard text generation, the ability to stream longer responses efficiently, and support for multi-turn chat experiences with memory persistence via session objects. Additionally, Gemini handles asynchronous tasks, making it suitable for workflows where responses do not need to follow a strict sequential order.

Controlling Model Behavior

Developers can fine-tune Gemini’s behavior using generation configuration options, such as adjusting temperature settings and defining system instructions to shape tone and creativity. Safety controls are also available, allowing developers to set thresholds to moderate and manage the output, ensuring alignment with compliance or brand guidelines.

Multimodal Input and Content Retrieval

Gemini is designed to handle a broad range of data types. It can ingest and interpret PDFs, audio, images, and video files, often directly from Google Cloud Storage URIs. Notably, it includes built-in YouTube integration, allowing users to ask questions or generate summaries directly from a video URL, streamlining media analysis and content understanding workflows.

Structured Output for Predictable Results

For applications requiring consistent data formatting, Gemini can generate structured outputs that align with predefined schemas using formats like Pydantic or OpenAPI. This capability facilitates easy integration with downstream systems and significantly reduces the need for manual data post-processing.

Building Intelligent Agents with Tools

Gemini extends its utility by enabling developers to build intelligent agents capable of interacting with external and internal systems. It integrates with Google Search to provide grounded answers with citations, and it connects with Vertex AI Search for querying proprietary, organization-specific datasets. Function calling allows Gemini to interact with APIs based on prompt input—ideal for dynamic task execution, though it requires comprehensive testing to ensure accuracy. Gemini also supports direct code execution, enabling it to perform calculations and return results in real time.

Conclusion: The Future of Generative AI Is Real-Time Data and Tool-Rich

By combining real-time data infrastructure with advanced model features, teams can dramatically improve the performance and reliability of generative AI applications.

Pascal Vantrepote’s emphasis on data freshness shows how streaming platforms and precise prompt engineering can eliminate hallucinations and democratize access to analytics. Lavi Nigam’s overview of Vertex Gemini demonstrates the power of structured outputs, multimodal input, and tool integrations to create intelligent, context-aware AI agents.

Together, these approaches are reshaping how organizations interact with data, automate tasks, and build AI systems that are scalable, accurate, and deeply integrated into business processes.