Many organizations are experimenting with AI to generate content faster. Far fewer are building AI systems that actually improve the learner experience.

This hands-on session focuses on designing and building a content-aware AI learning assistant that answers questions using your own indexed learning materials. Rather than relying on generic large language model responses, you will create a retrieval-based assistant that draws only from structured course content, design documents, or training materials. During this session, you will actively design and build a retrieval-based assistant by chunking content, indexing it, connecting it to an LLM API, and implementing guardrails to constrain responses.

This session emphasizes architecture and implementation patterns over product promotion. While a specific search platform and LLM will be demonstrated for simplicity, the approach applies to any modern search and LLM provider. You will leave with a working prototype, a reusable content-chunking framework, retrieval prompt templates, and a practical deployment blueprint for embedding AI into an LMS, knowledge base, or customer education platform. 

In this session, you will learn how to:

• Use an LLM to chunk and structure learning content for retrieval-based AI
• Connect indexed content to a language model to reduce hallucination
• Design guardrails that constrain AI responses to approved learning materials
• Prototype and test a learner-facing AI assistant
• Plan integration of retrieval-based AI into a learning platform

This session is for learning professionals ready to move from AI experimentation to real implementation. You should be familiar with basic AI tools (e.g., ChatGPT, Gemini) and general LMS functionality. No coding skills are required, but you should be comfortable following structured configuration steps and working with web-based tools and APIs. This session moves beyond simple prompting and introduces retrieval-based AI architecture (often referred to as RAG) in an applied format.

Technology Required:
Laptop
LLM