WTH is RAG and why should you care?

We hear the acronym RAG tossed around increasingly in AI discussions. If you’re a mid-market leader trying to figure out how Generative AI (gen AI) actually delivers value, you might be thinking, “WTH is RAG, and why should I care?”

Here’s the plain English answer, and why it’s the secret sauce for delivering AI solutions that actually deliver on their objectives.

At a high level, the AI models everyone talks about, Large Language Models (LLMs) and Foundation Models (FMs), are trained on massive amounts of public data, but only up to a certain point in time.

The Problem: Out-of-the-box models don’t know anything about your business. They can’t access your:

• Your latest inventory reports.
• Your specific customer service scripts.
• Your internal HR policies.
• Your brand’s unique voice and ethical parameters.

If you ask a generic AI chatbot, “What’s the process for approving a new product idea?” it might give you a boilerplate answer from a university textbook. That’s unlikely to be useful to your specific circumstance.

RAG is how you can address this.

Retrieval Augmented Generation (RAG) is a technique that lets an LLM access your company’s most recent, private data to provide a more accurate and relevant response. It essentially grounds the model with current, domain-specific context.

RAG is a core component of the customised, differentiated AI solutions The Distillery builds for mid-market transformation. It is how we turn a generic technology into a strategic asset.

1. Context and Accuracy: RAG enables the AI to “look up” facts in your secure knowledge sources (like internal databases, document repositories, or vector databases) before generating an answer. This dramatically reduces factual errors or “hallucinations”.
2. Instant Productivity: We use RAG to build internal applications that instantly answer employee questions using natural language queries (NLQs). Imagine a new hire asking a corporate chatbot, “What’s the deductible on my health insurance?” and getting an accurate, instant answer based on the current HR policy document – not general internet knowledge.
3. Customer Experience: For customer-facing solutions, RAG means the chatbot or virtual agent can access real-time information to solve problems. For instance, a travel agency application can use RAG to access the customer’s past trips, preferences, and current trip inventories to create a personalised recommendation.

Agility: You don’t have to spend a fortune constantly retraining the entire AI model every time a dataset changes. RAG simply provides the most up-to-date context at the time of the query.

For us, RAG isn’t just a technical term; it’s a critical pillar of our data strategy for our clients.

We know that true competitive advantage doesn’t come from using the latest shiny toy; it comes from using that toy to leverage the thing that makes your business truly unique: your data.

The Distillery focuses on the complete, end-to-end data strategy that makes RAG (and all gen AI) work. This includes:

• Establishing the Data Foundation: Ensuring your data is secure, governed, and readily accessible for RAG through integrated systems.
• Customisation: Determining the right mix of RAG and other techniques, like fine-tuning, to align the AI’s output with your specific business goals, brand voice, and customer experience.
• Process and Governance: Implementing the human oversight needed to ensure the AI’s output is safe, compliant, and reflects how you want the world to perceive your business.

If you’re serious about moving past AI pilots and deploying customised solutions that provide a tangible, measurable uplift in productivity and customer experience, RAG is where the rubber meets the road.

Ready to transform your business with AI solutions grounded in your unique data?

We help mid-market leaders build the data strategy and customised applications that turn data from an asset into results. Let’s chat about your next critical path forward.