The business world has moved past the hype phase of AI. The question is no longer if organizations should adopt AI, but how to do so in a way that delivers real value.
At Flywheel Strategic, we’ve found that a lot of conversations about AI stall when teams are uncertain about the underlying architecture. Two of the most common approaches are the familiar Large Language Models (LLMs) and then there is also Retrieval-Augmented Generation (RAG). These two AI technologies solve different problems and require different design considerations.
What is an LLM?
Large Language Models (like ChatGPT) are trained on massive datasets, allowing them to generate human-like text, answer questions, and even draft complex content. Their strength lies in pattern recognition: they can take prompts and produce remarkably fluent responses.
But here’s the catch:
LLMs are only as current as the data they were trained on.
They can "hallucinate" answers that sound right but aren’t factually grounded.
They lack organizational context unless you specifically embed it.
On their own, LLMs are powerful generalists that are good for ideation, summarization, and automating routine tasks. But when accuracy and domain-specific knowledge matter, they need reinforcement.
Enter RAG: Retrieval-Augmented Generation
Bill Rogers, CEO of ai12z discusses what RAG is and some of its benefits.
Retrieval-Augmented Generation (RAG) bridges the gap. Instead of relying solely on what the model “remembers,” RAG architectures pull in context from trusted sources such as your own knowledge base, CRM, intranet, product catalog, or regulatory documents; before generating a response.
Think of it this way:
LLM alone is like a talented but improvisational speaker.
RAG is that same speaker, but with a research assistant handing them vetted facts in real time.
The business advantages are clear:
Accuracy: Responses are grounded in your organization’s actual data.
Currency: Content reflects the latest updates, not a model frozen in time.
Trust: Reduces the risk of AI hallucinations leading to bad decisions.
Where Each Fits in Business Workflows
LLM strengths:
Brainstorming marketing copy.
Automating standard communications.
Summarizing lengthy reports or meetings.
RAG strengths:
Customer support chatbots that must reflect your latest product specs.
Internal tools that retrieve HR policies, IT procedures, or compliance rules.
Sales enablement systems that surface case studies or competitive intelligence.
Most organizations don’t choose between an LLM or a rag solution RAG, but instead they combine them. The real art lies in architecting a system that balances creativity with reliability.
Scott explains how ai12z works, leveraging RAG when appropriate.
Flywheel’s Perspective
We see AI adoption less as a technology race and more as a strategy question: How do you ensure the system aligns with your business goals, workflows, and data realities?
That’s where Flywheel comes in. Our role is to design AI experiences that:
Fit seamlessly into existing workflows.
Balance innovation with operational trust.
Scale from proof-of-concept to enterprise-ready.
In some cases, a stand-alone LLM is enough. In others, RAG is essential. Often, the solution is hybrid. The key is understanding the distinction—and having the expertise to architect accordingly.
The difference between LLM and RAG isn’t just technical. It’s the difference between dabbling in AI and building business-critical solutions that people trust.
At Flywheel, we help organizations make that leap with clarity and confidence.