What RAG is (and isn’t): Quick background
George
Chief of AI at Moveo
September 3, 2025
in
✨ AI Deep Dives
Welcome to the first chapter of our series, "AI Deep Dives"!
In the current landscape of AI, where 95% of generative AI pilots fail to deliver significant financial returns, it’s crucial to look beyond the hype and truly understand the technologies that drive success.
In this chapter, we'll take a deep dive into Retrieval-Augmented Generation (RAG), what it is and, more importantly, what it isn't, so you can start to understand the complexities of enterprise AI with a solid foundation. Let's get started!
Retrieval-Augmented Generation (RAG) is a powerful technique that improves the answers of Large Language Models (LLMs) by "anchoring" them in a company's specific documents.
Imagine RAG as a system that indexes your static knowledge (such as FAQs, policy PDFs, knowledge base articles, and product manuals) and, at query time, pulls the most relevant passages so the model can ground its answer in your own content.
This provides clear benefits, such as improving the freshness (as you update your documents, the model’s answers also update) and relevance of the information. However, it’s vital to understand that on its own, RAG doesn’t guarantee correctness, safe actions, or compliance.
A simple example of RAG
Imagine a customer asking their bank’s chatbot, “What’s the interest rate for the Premier Savings Account?”. Without RAG, the model might make an educated guess based on its training, which could be outdated or inaccurate. With RAG, the system retrieves the bank’s official documents, finds the current rate, and then generates a clear, conversational response.
It’s a bit like having a smart intern: instead of relying only on memory, they quickly check the company’s files before answering, so you always get the right information, in plain language.
What RAG Isn’t
While Retrieval-Augmented Generation is powerful for improving the freshness and relevance of answers, it’s not a silver bullet for enterprise needs.
RAG can pull the right passage from a fee schedule, a benefits summary, or a compliance playbook, but it does not guarantee that the answer will be correct, compliant, or safe to act on.
Just as importantly, RAG is not a workflow engine. It cannot enforce multi-step business processes such as authentication, consent gates, dispute resolution, or refunds — all of which require strict ordering, validation, and auditability.
For example, in banking, retrieving overdraft-fee rules doesn’t ensure the system actually follows the correct refund process. In insurance, surfacing a benefits summary doesn’t guarantee that pre-authorization is checked before approving a treatment. And in debt collection, fetching disclosure templates won’t stop an assistant from skipping consent or fabricating plan details.
Put simply: RAG improves the information available, but it doesn’t ensure that information is applied in the right way, in the right order, with the safeguards enterprises demand.
From concept to application: Examples of RAG in Critical Sectors
As O’Reilly argues, burying business logic inside prompts creates solutions that are unreliable, hard to govern, and impossible to maintain at scale.
True innovation for enterprises isn’t about "smarter prompts," but about robust systems that separate conversation from controlled execution. RAG is a crucial first step in this direction.
This distinction between what RAG can and cannot do is critical in regulated industries. In finance, insurance, or collections, missing a step, skipping a disclosure, or misstating policy isn’t just inconvenient; it creates compliance, legal, and reputational risks
To illustrate how RAG works in practice, by applying structured knowledge in high-risk sectors, let's explore a few examples:
Banking: Precision in financial information
In the banking sector, RAG can be used to pull the Fee & Dispute FAQs and Cardholder Agreement to answer “How long do I have to dispute a charge?” or “What’s your overdraft-fee refund policy?” with citations to those documents.
However, while RAG can fetch policy text, it doesn't guarantee the model won’t misstate refund eligibility or skip required disclaimers.
Insurance: Policy details at your fingertips
In the insurance sector, RAG is valuable to retrieve the member’s Plan Brochure and Benefits Summary (copays, pre-auth rules, exclusions) to answer “Am I covered for an MRI?” or “Do I need pre-authorization for physical therapy?”.
However, RAG alone won’t enforce pre-authorization workflows or verify eligibility gates.
Debt Collection: The sensitivity of compliance
In a domain as regulated and sensitive as debt collection, RAG can help to use your Compliance Playbook and Disclosure Templates (permitted contact hours, required language) to answer “Can you contact me after 8 PM?” or “What disclosures apply if I set up a payment plan?” with the exact, approved wording.
While RAG can fetch disclosure templates, it won’t guarantee the agent asks for consent or avoids a coercive tone.
Learn more: AI Collection Agents vs Human Debt Collectors
RAG is a step, not the complete solution
RAG is, without a doubt, a valuable component in the context of enterprise AI, offering an effective way to "anchor" LLMs in your company's specific knowledge. It improves the freshness and relevance of answers, which is a big leap forward in many applications.
However, it's vital to recognize its limitations: it’s not a guarantee of correctness, safe actions, or compliance, especially in critical business contexts.
In the next chapter of our "AI Deep Dives" series, we will dive even deeper, exploring "Chapter 2 — The great AI debate: Wrappers vs. Multi-Agent Systems in enterprise AI". Prepare to discover how to go beyond RAG and build robust, reliable AI systems that truly deliver value in the enterprise world.