As excitement surrounding generative AI continues, naturally, an increasingly important conversation inside enterprise organizations follows: How do we trust the answers AI gives us?
That question sits at the center of a growing distinction between traditional Large Language Models (LLMs) and Retrieval Augmented Generation (RAG).
Public AI tools have demonstrated extraordinary capabilities, but in context, reliability, transparency, and accuracy matter just as much, and sometimes more.
Check out the studio session between Drew Lent and Mike Marolda from Progress Agentic RAG and Scott Snowden as they explore exactly that.
The Difference Between LLMs and RAG
LLMs are trained on enormous volumes of public and licensed information. They are exceptionally good at pattern recognition, language generation, summarization, and reasoning. But they don't inherently know your business.
They don't understand your internal documentation, product specifications, policies, customer data, implementation frameworks, or operational nuances unless that information is specifically introduced into the conversation. They're also designed to generate plausible answers, meaning they can present incorrect information with confidence, a phenomenon now widely known as hallucination, all in an often sycophantic wrapper.
RAG approaches the problem differently.
Rather than relying solely on the model's generalized training, RAG systems retrieve information directly from approved enterprise content before generating a response. That information becomes the contextual grounding for the answer.
As Drew from the Progress Agentic RAG team puts it: "It's augmenting that LLM context by retrieving your own information."
Therefore, the key difference is that RAG shifts AI from being broadly knowledgeable to being contextually trustworthy.
"But wait... isn't every enterprise GPT already doing retrieval?"
Sort of. A GPT with uploaded files is technically a form of RAG, and ChatGPT's "Knowledge" feature behaves similarly at a small scale, but it typically lacks the same level of architecture, governance, scalability, and retrieval controls required for enterprise deployment.
RAG systems are built to solve problems that smaller scale implementations struggle with:
- permission and access controls
- source governance
- freshness of data
- citations and traceability
- multi repository retrieval
- chunking and indexing strategy
- deterministic retrieval pipelines
- orchestration across systems
- and more
While a custom GPT with uploaded PDFs is usually:
- static
- small scale
- manually curated
- difficult to govern
You can plug your entire website into a RAG pipeline. A custom GPT alone is typically not designed to manage enterprise scale retrieval, indexing, governance, and synchronization.
Enterprise AI Challenges: Why Trust Is A Big Hurdle
Everyone knows AI is powerful, but not everyone agrees on whether it can be trusted enough to integrate into operational workflows, customer experiences, and decision making environments.
Flywheel's direct research ahead of the Digital Momentum Summit reveals a telling gap. While 53% of businesses surveyed said they're experimenting with AI, only 27% reported deploying it in meaningful ways.
That discrepancy reflects organizational caution.
And it's understandable. There's too much at stake to deploy systems capable of generating inaccurate information, especially in regulated industries, or across Your Money Your Life categories (Google's equivalent to anything that impacts finance, health, legal, and insurance).
All it takes is one hallucinated answer to erode confidence, which RAG is better suited to constrain, since it is designed to ground responses in approved enterprise knowledge.
How RAG Is Redefining Search and Discovery
Another shift, beneath the surface, is how user interactions are changing. Historically, enterprise websites and knowledge systems relied on navigation structures, keyword search, and manual exploration. Users were responsible for assembling meaning themselves.
RAG is a fundamentally different interaction model.
Instead of hunting through pages, documents, and links, users can ask conversational questions and receive synthesized, contextual responses grounded in (while being cited) enterprise knowledge.
This means less clicking of blue links, opening PDFs and searching for keywords related to the knowledge you're after, and more streamlined and meaningful discovery.
And this aligns with user expectations; customers want answers, not navigation. Employees want intelligent internal systems, not friction. Partners expect fast access to expertise.
Final Thoughts
While early AI conversations focus on possibility, another phase is underway, focusing on what AI can do reliably.
That's where RAG has an advantage. In the distinction between generating versus grounding answers.