M5: Practical AI for Safety: Deploying a RAG Model
Wed, 6/3: 10:30 AM - 11:30 AM CDT
Education Sessions
Education Sessions
Ernest N. Morial New Orleans Convention Center
Room: 295
CM Credit Hours: 1
Description
This session will introduce participants to the concepts of Retrieval-Augmented Generation (RAG) and demonstrate how this emerging AI approach can be applied to industrial hygiene and safety. RAG combines semantic document search with large language models to generate clear, context-based answers, offering a practical way to navigate complex technical and regulatory information.
Rather than focusing only on the end product, the session will walk through the process of building a simple RAG workflow-from breaking documents into chunks, creating vector embeddings, and storing them for efficient retrieval, to generating responses through an AI model. Tools such as Streamlit for rapid prototyping, GitHub for code sharing, and cloud storage for document handling will also be highlighted.
Participants will leave with a clear understanding of how RAG works, the resources needed to implement it, and ideas for applying it to their own safety and health challenges.
Rather than focusing only on the end product, the session will walk through the process of building a simple RAG workflow-from breaking documents into chunks, creating vector embeddings, and storing them for efficient retrieval, to generating responses through an AI model. Tools such as Streamlit for rapid prototyping, GitHub for code sharing, and cloud storage for document handling will also be highlighted.
Participants will leave with a clear understanding of how RAG works, the resources needed to implement it, and ideas for applying it to their own safety and health challenges.
Learning Outcomes
Upon completion, the participant will be able to:
• Define Retrieval-Augmented Generation (RAG) and its relevance to occupational and environmental health and safety (OEHS) practice.
• Differentiate RAG workflows from traditional document search methods used in accessing safety and regulatory information.
• Illustrate the process of document chunking, embedding, and vector storage as key steps in building a RAG system.
• Apply readily available tools such as Streamlit, GitHub, and embedding models to prototype a simple RAG-based question-answering assistant.
• Evaluate opportunities for integrating RAG into OEHS workflows to improve efficiency, accuracy, and consistency in safety communication.
• Define Retrieval-Augmented Generation (RAG) and its relevance to occupational and environmental health and safety (OEHS) practice.
• Differentiate RAG workflows from traditional document search methods used in accessing safety and regulatory information.
• Illustrate the process of document chunking, embedding, and vector storage as key steps in building a RAG system.
• Apply readily available tools such as Streamlit, GitHub, and embedding models to prototype a simple RAG-based question-answering assistant.
• Evaluate opportunities for integrating RAG into OEHS workflows to improve efficiency, accuracy, and consistency in safety communication.
Content Level
Introductory
Core Competencies
Functional Core Competencies
IH/OH Program Management
Safety
Keywords
Education and training
OHS Management systems, auditing, and compliance
Risk assessment and management
Safety
Session Availability
In-person
OnDemand
Virtual
Targeted Audience
Practitioner
Transfer of Knowledge
Lecture Only