Generative AI—the groundbreaking technology behind platforms like ChatGPT, Copilot, and Gemini—is rapidly becoming the foundation for all modern AI interactions. It represents a technological shift comparable to the internet or the smartphone. For HR and benefits leaders, it promises a future where administrative burdens are lifted and employees finally understand their benefits packages.
In part 1 of our AI in Benefits 101 series, we explained how this technology works as a powerful engine for conversation. Now, we turn to the next critical question: how do we make it accurate and safe for healthcare?
To answer that, we have to look beyond the "magic" of the conversation and understand how the technology actually accesses information.
To understand the solution, we must appreciate the engine. Generative AI is the basis for all modern AI systems. It is trained on vast amounts of text from the open internet, allowing it to understand human intent, summarize complex topics, and generate natural, conversational responses.
Think of Generative AI as a brilliant communicator. It is excellent at the "form" of communication—it can write a poem, draft an email, or explain a concept in simple terms. But because standard models are trained on public data, they lack private context.
A standard, public Generative AI model does not know your company’s specific plan design, your negotiated rates, or your current provider directory. It hasn't read your summary plan description (SPD). Because of this, a standard model cannot reliably answer questions like "Is Dr. Smith in my network?" or "What is the deductible for this procedure?"
To solve this, we don't stop using Generative AI. We enhance it. The solution is a framework called Retrieval-Augmented Generation (RAG).
RAG is an AI framework that enhances a Large Language Model (LLM) by giving it access to external, up-to-date, and proprietary information before it generates a response. It combines the conversational power of Generative AI with the accuracy of a search engine.
RAG connects the language skills of Generative AI to a secure library of your up-to-date information. Here is the difference:
In the benefits space, "close enough" is not enough. A hallucinated answer regarding coverage or costs can lead to surprise bills and eroded trust.
By utilizing the RAG framework, the AI is forced to retrieve the facts first. It references your specific eligibility files, carrier networks, and formularies. Only once it has retrieved the correct data does it use Generative AI to craft the response. This creates an experience where the AI interacts with the natural fluency of a human but the strict accuracy of a database.
Using a RAG framework within a private ecosystem also solves the privacy challenge. Public AI models generally cannot be trusted with Protected Health Information (PHI) because the data might be used to train the public model.
However, when a company uses a private RAG architecture, the AI processes the data in a secure, closed loop. It allows HR leaders to utilize the intelligence of modern AI without ever exposing member data to the public internet.
At HealthJoy, we leverage this RAG framework to power JOY, our virtual benefits assistant. It ensures that while we use the most advanced Generative AI to understand employees’ intent, every recommendation they receive is grounded in verified, company-specific truth.
It’s the best of both worlds: the ease of conversation that employees want, combined with the rigor and compliance that HR demands.
Now that we understand how RAG bridges the gap between Generative AI and benefits accuracy, our final article of this series will look at the results. We’ll explore how this technology reduces administrative burden and finally elevates HR teams into the strategic roles they were hired for.