RAG Explained: How Businesses Use Their Own Data with AI
Retrieval-Augmented Generation (RAG) is the technique that transforms a generic AI model into one that knows your business, your products, your customers, and your data — without training a custom model.
Why Generic AI Is Not Enough
GPT-4o and Claude are extraordinarily capable at general tasks. But they know nothing about your specific products, your customer history, your internal processes, your pricing, or any information created after their training cutoff. When you ask them questions that require your specific knowledge, they either refuse to answer or — worse — hallucinate plausible-sounding but incorrect information.
Fine-tuning a model on your data is one solution, but it is expensive, slow, and does not update dynamically as your knowledge base changes. RAG is the practical alternative: give the AI access to your knowledge at query time, not training time.
The Three-Step Process
RAG works by finding the relevant information before asking the AI to answer.
Index your knowledge base
Take all the documents, articles, FAQs, product descriptions, or data records you want the AI to know. Convert each piece of text into a numerical vector (called an embedding) using an embedding model like OpenAI’s text-embedding-3-small. Store these vectors in a vector database or as a field in your regular database.
Retrieve the most relevant context
When a user asks a question, convert their question into the same kind of embedding vector. Find the 3-5 documents in your knowledge base whose vectors are most similar (using cosine similarity). These are the documents most likely to contain the answer.
Augment the AI prompt with retrieved context
Pass the user’s question to the AI model — but include the retrieved documents as context in the prompt. Instruct the AI to answer based on the provided documents rather than its general training. The AI now answers with your specific knowledge, not generic knowledge.
The key insight: the AI is not memorising your data. It is reading the relevant parts at the moment it needs them — just as a human employee looks up information before answering a customer question, rather than memorising the entire knowledge base.
A No-Code Implementation
Step 1: Knowledge Base data type
Create a Knowledge Base data type in Bubble with fields: title (text), content (long text), category (option set), embedding (long text). Populate with your FAQs, product docs, policy documents, or any content the AI should know.
Step 2: Generate and store embeddings
For each Knowledge Base record, call OpenAI’s Embeddings API with the content field. Store the returned embedding array (a large list of floating point numbers) as a long text field. Run this for all existing records and for every new record added.
Step 3: Semantic search workflow
When a user submits a question, call the Embeddings API with their query. In a backend workflow, calculate cosine similarity between the query embedding and every Knowledge Base record’s embedding. Return the top 3 matching records.
Step 4: Augmented generation
Pass the user’s question plus the 3 retrieved Knowledge Base records to GPT-4o or Claude. Instruct the AI: ‘Answer the user’s question using only the provided context. If the context does not contain the answer, say so.’ The AI produces an accurate, grounded answer.
📌 For larger knowledge bases (1,000+ records), cosine similarity calculations over all records in a Bubble backend workflow become slow. At scale, use a dedicated vector database (Pinecone, Weaviate, or pgvector) for the similarity search step.
| Use Case | Knowledge Base Content | User Interaction | Business Value |
|---|---|---|---|
| Customer support chatbot | Help articles, FAQs, product documentation | Customer asks a support question | Resolves 40-60% of queries without human agent |
| Internal knowledge assistant | SOPs, HR policies, process docs, meeting notes | Employee asks a policy or process question | Reduces time spent searching internal wikis |
| Sales enablement tool | Product specs, competitive analysis, case studies, pricing | Salesperson asks how to handle an objection | Faster, more accurate sales responses |
| Legal document assistant | Contract templates, legal precedents, compliance requirements | Lawyer asks about a specific clause type | Faster document review and drafting |
| Product documentation Q&A | Technical docs, API references, release notes | Developer asks a technical question | Reduces support load; improves developer experience |
Choosing the Right Approach
Use RAG when…
- Your knowledge base changes frequently (new products, updated policies, recent events)
- You need to cite sources so users can verify AI answers
- Your knowledge base is large and varied in topic
- You want to get started quickly without model training infrastructure
- Data privacy requires keeping documents out of model training
Use fine-tuning when…
- You want the AI to adopt a specific writing style or persona consistently
- You have thousands of labelled examples of correct input/output pairs
- The task is narrow and well-defined (a specific classification or extraction task)
- Latency is critical and you need a smaller, faster model for a specific task
- Your knowledge is stable and does not change frequently
Want a RAG System Built for Your Business Knowledge Base?
SA Solutions builds RAG-powered AI assistants in Bubble.io — turning your existing documentation, FAQs, and product knowledge into an intelligent, queryable system.
