Building GenAI Application using LabVIEW LLM Libraries

Learn the fundamentals of large language models (LLMs) and how they can be invoked directly within LabVIEW. This session explains key model types (text-to-text, image-to-text, and embeddings) and demonstrates how LabVIEW makes AI accessible to engineers without deep data science expertise.

Discover how to abstract different LLM providers—like OpenAI, Azure OpenAI, or Google Gemini—into a unified LabVIEW interface. You’ll see how model abstraction simplifies switching between providers and keeps your applications future-proof.

Understand how to engineer context so that LLMs can work effectively with your data. This lesson shows techniques for injecting domain knowledge, and preparing contextual information so models deliver accurate, relevant responses.

Go beyond simple prompts by integrating Retrieval Augmented Generation. Learn how to combine external knowledge bases with LLMs inside LabVIEW, enabling more accurate and domain-specific answers. A step-by-step demo illustrates how RAG strengthens real-world applications.

Explore how to build autonomous agents that can execute tasks, using LabVIEW. You’ll learn the agent design, see practical use cases, and build your own agent that interacts intelligently with your data.

Learn best practices for cleaning, structuring, and preparing data so it’s AI-ready. This session covers pipelines for data source reading, embedding generation, indexing, and storing in vector database, ensuring your LabVIEW applications can scale reliably with quality data.

Bring everything together into a user-friendly experience. You’ll learn how to embed a chat-style interface into LabVIEW applications, giving end-users an intuitive way to interact with models, agents, and knowledge sources.

Close the series by addressing security and privacy. This lesson explains how to run local or offline models inside LabVIEW—ensuring sensitive data never leaves your environment while still benefiting from LLM capabilities.