RAG
Ask a general AI about your business and you'll get a confident answer based on nothing — industry averages, plausible guesses, things it read during training. RAG changes that. It forces the AI to retrieve your actual data before generating a response. Instead of 'behavioral health orgs typically see 5-10% denial rates,' you get 'your denial rate was 8.2% last quarter, up from 6.1%, driven by documentation issues at your Eastside location.' The difference is the AI is answering from evidence, not memory. Without RAG, AI sounds smart. With RAG, AI is actually useful.
Go deeper
Your COO asks: 'What was our average time-to-fill for technician positions last quarter, and how does that compare to what we discussed in the September leadership meeting?' Without RAG, an AI gives you a generic answer about industry hiring benchmarks. With RAG, it pulls your actual HR data showing 34 days average time-to-fill (up from 28), then retrieves the September meeting transcript where the team committed to a 25-day target and agreed to add a signing bonus. Now you have the full picture — the number, the context, and the gap between commitment and reality — in one answer.
The mistake companies make is thinking RAG is just 'search.' Basic search finds documents that contain your keywords. RAG finds the right information, synthesizes it across multiple sources, and delivers a coherent answer. The quality of RAG depends entirely on what you connect it to. If your documents are disorganized, your data is ungoverned, and your meeting notes live in 14 different systems, the retrieval step fails and the AI falls back to guessing.
Questions to ask
- What are the top five data sources our team references when making decisions — and are those sources accessible to an AI system?
- When the AI answers a question, can it show us exactly which documents and data points it used?
- What happens when two sources contradict each other — does the system flag the conflict or silently pick one?