A lot of people ask how we make our AI assistants sound natural while staying clinically accurate. The truth is, it's not just about the model.

We use something called RAG, short for Retrieval-Augmented Generation, to make sure our agents can answer questions safely—even in complex medical situations. If you're building AI for healthcare, this is one of the most important tools to understand.

Here’s how it works, and how we use it at Puppeteer.

The Problem: Language Models Make Things Up

Language models are powerful. But they have a major flaw: they sometimes make things up.

They’ll answer a question even when they don’t know the answer, filling in the blanks with what sounds right. In healthcare, that’s a big risk. You can’t have an AI agent guessing how long someone should fast before surgery or giving advice about insulin without certainty.

We needed something smarter.

What RAG Actually Does

RAG is straightforward.

Instead of relying only on what the AI remembers, we first pull in relevant documents—things like clinical protocols, FAQs, or discharge instructions—and then let the AI use that information to craft a grounded, accurate response.

It’s like giving the AI a real-time cheat sheet.

It doesn’t need to memorize everything. It just needs to know where to look.

How We Use RAG at Puppeteer

We build voice and chat agents for clinics and digital health companies. These agents handle intake, follow-ups, medication questions, and more.

Every provider has their own protocols, tone, and goals.

So instead of retraining a model every time something changes, we give each agent a private knowledge base. That’s where RAG comes in.

When a patient asks something like, "When can I start exercising after my procedure?", the agent searches that provider’s documentation, finds the answer, and builds a response based on it.

We support:

• PDFs and internal guides

• Transcripts from educational videos

• Structured content like treatment plans

• Real-time updates, no redeploy required

It’s fast, safe, and tailored to each clinic’s workflow.

Why It Matters

In practice, this means:

• The agent avoids guessing on clinical topics

• New content can be used immediately

• Providers stay in control of the answers

• Patients get reliable, accurate responses

This isn’t just about being correct. It’s about trust.

Patients want to feel heard. Providers want to feel confident. RAG helps us deliver both.

Bonus: It Scales Without Burning Out Your Team

RAG lets us scale knowledge without scaling staff.

A great nurse might answer the same question ten times a day. With RAG, we can encode that once and let the AI handle it a thousand times. No extra burden. No burnout.

And every response is traceable. You always know where the information came from.

What’s Next

We’re already working on the next layer:

• Personalizing responses based on symptoms or diagnosis

• Native support for more languages

• Pulling insights from wearable data or labs

There’s more to come. But even now, combining LLMs, RAG, and your own content is a big step forward in making healthcare AI safer and more useful.