The most common concern businesses have when deploying AI support is accuracy. What if the AI makes something up? What if it gives wrong pricing or outdated policy information? This is where RAG — Retrieval-Augmented Generation — solves the problem.
What RAG Actually Means
RAG is an architecture where the AI doesn't answer from memory — it answers by first retrieving relevant content from your documents, then generating a response grounded in that content. The result: answers that are accurate, citable, and updatable without retraining the model.
How It Works in Practice
Step 1: Upload Your Docs
You upload your knowledge sources — PDFs, Word documents, plain text files, or URLs. Viziqo Assist supports all common formats. The system chunks your content into searchable segments.
Step 2: Embed and Index
Each chunk is converted into a vector embedding — a numerical representation that captures semantic meaning. These embeddings are stored in a vector database (Viziqo uses Qdrant).
Step 3: Retrieve at Query Time
When a customer asks a question, the query is embedded and the system retrieves the most semantically similar chunks from your knowledge base. Not keyword matching — meaning matching.
Step 4: Generate a Grounded Answer
The LLM receives the retrieved chunks alongside the customer's question and generates a response grounded in your actual content. If the answer isn't in your docs, it says so rather than guessing.
What to Upload
- The quality of your AI depends entirely on the quality of your knowledge base. Prioritise:
- Product documentation — features, specs, how-to guides
- FAQ content — the questions your support team answers 50 times a day
- Pricing and plans — keep this updated whenever pricing changes
- Policies — refunds, shipping, cancellation, privacy
- Troubleshooting guides — step-by-step resolution paths
Keeping It Current
The biggest advantage of RAG over fine-tuning: you don't retrain to update. Change a price, update a policy, add a new product — upload the new document and the AI reflects it immediately. No ML pipeline, no waiting.
The Bottom Line
RAG is what makes AI support trustworthy. It's the difference between a chatbot that confidently makes things up and one that accurately represents your business. If you're evaluating AI support platforms, ask specifically how they handle knowledge grounding — the answer tells you everything.