RAG is a technique for giving AI models access to relevant information from your own documents and data at query time. Here's how it works and when to use it.
RAG — Retrieval-Augmented Generation — is a technique for giving AI models access to your own data when they answer questions.
Out of the box, an LLM only knows what it learned during training, up to its knowledge cutoff date. It does not know your business, your products, your internal processes, or anything written after training ended. RAG fixes this.
Phase 1 — Indexing (setup)
Phase 2 — Retrieval and generation (at query time)
The result: an AI that answers questions accurately using your current, specific information.
| | RAG | Fine-tuning | |---|---|---| | How it works | Retrieves knowledge at query time | Bakes knowledge into model weights | | Data stays current? | Yes — update the index | No — requires retraining | | Cost | Low (vector storage + embedding calls) | High (training compute) | | Setup time | Hours to days | Days to weeks | | Best for | Dynamic, frequently updated knowledge | Specific style or behaviour shifts |
For most business applications, RAG is the right choice. Fine-tuning is appropriate when you need to shift the model's writing style, domain vocabulary, or behaviour — not just give it new facts.
Internal knowledge chatbot — employees ask questions in natural language; the AI answers from internal documentation, policy documents, and process guides. Reduces repetitive questions to HR, legal, or IT.
Customer support automation — the AI answers customer questions from product documentation, FAQs, and known issues. Stays accurate as documentation changes.
Contract and document review — feed in a library of clause examples, precedents, or compliance requirements; the AI reviews new documents against that library.
Sales enablement — sales team asks questions about pricing, competitors, or product specs; the AI answers from current internal documentation rather than training data.
WhatWill AI builds RAG-based AI systems for businesses — internal knowledge bots, document processing, and more. Book a discovery call to discuss what your data could power.
RAG stands for Retrieval-Augmented Generation. It is a technique for giving a large language model access to relevant information from an external knowledge base — your documents, database, or knowledge base — at the time it answers a question. Rather than relying solely on what the model learned during training, it retrieves relevant content and includes it in the prompt, allowing the model to give accurate, up-to-date, and domain-specific answers.
A RAG system works in two phases. First, at setup time, your documents are split into chunks and converted into vector embeddings (numerical representations of meaning) stored in a vector database. Second, at query time, the user's question is also converted to an embedding, and the most semantically similar document chunks are retrieved. Those chunks are added to the prompt sent to the LLM, which then generates an answer based on both the retrieved content and its training knowledge.
RAG is faster, cheaper, and more flexible than fine-tuning for most use cases. Fine-tuning trains the model on your data — it is expensive, slow, and produces a model that may drift over time as your data changes. RAG keeps the base model unchanged and retrieves current information at query time, so it stays up to date as your knowledge base changes. RAG is the right choice when accuracy and up-to-dateness matter and your knowledge base changes regularly.
Common business use cases: internal knowledge base chatbots (employees ask questions and the AI answers from company documentation), customer support (AI answers questions from product documentation), contract review (AI analyses contracts against a library of clause examples), compliance checking (AI reviews documents against policy requirements), and sales enablement (AI answers prospect questions using current pricing and product documentation).
The key components are: a document processing pipeline to split and embed your documents, a vector database to store the embeddings (Pinecone, Weaviate, pgvector, or Chroma are common), an embedding model to convert text to vectors, an LLM to generate answers, and an orchestration layer to tie it together. Tools like LangChain and LlamaIndex provide prebuilt RAG pipelines. n8n supports RAG workflows with its AI nodes. The complexity scales with how well-structured your source documents are.
WhatWill AI builds and runs AI systems for Australian businesses. Book a free 30-minute discovery call — we’ll tell you exactly what’s worth building for your situation.