How accurate are RAG systems compared to standard ChatGPT?

RAG systems are significantly more accurate for questions about your specific data because they retrieve actual source documents before generating answers. Well-implemented RAG systems achieve 85-95% accuracy on domain-specific questions, compared to standard LLMs which frequently hallucinate when asked about information outside their training data. RAG also provides citations, so you can verify answers.

How much data do I need to build a RAG system?

RAG systems work well with as few as 50 pages of documents, though they become increasingly valuable as the knowledge base grows. There is no practical upper limit — RAG systems can handle millions of documents. The key is having documents that are relevant to the questions users will ask, not just volume for the sake of it.

How long does it take to build a RAG system?

A basic RAG system with a single document collection can be built and deployed in 2-4 weeks. More complex implementations involving multiple data sources, access controls, and custom interfaces typically take 6-12 weeks. The longest phase is usually document preparation and chunking strategy optimisation, not the technical build itself.

Can RAG systems handle sensitive or confidential data?

Yes, with appropriate security measures. RAG systems can be deployed on Australian-hosted infrastructure, with access controls that ensure users only retrieve documents they are authorised to see. Embedding models can be run locally for maximum data privacy. The key is choosing the right architecture — self-hosted vector databases, Australian cloud regions, and LLM providers with appropriate data processing agreements.

What is the ongoing cost of running a RAG system?

Ongoing costs include LLM API usage (typically $200-2,000 AUD/month depending on query volume), vector database hosting ($50-500/month depending on scale), and embedding costs for new documents. Total monthly costs for a mid-sized deployment serving 50-200 users are typically $500-3,000 AUD. This compares favourably to the hours of staff time saved searching for information manually.

Document Search Systems Explained: How to Build an AI That Knows Your Business

Retrieval-Augmented Generation (RAG) is an AI architecture that connects a large language model to your organisation's own data, allowing it to answer questions accurately using your actual documents, policies, and knowledge — rather than relying solely on its training data. RAG solves the hallucination problem by grounding the AI's responses in real, retrievable sources. If ChatGPT is a knowledgeable generalist, a RAG system is a specialist that has read every document your organisation has ever produced.

Why do standard AI models hallucinate, and how does RAG fix it?

Large language models like GPT-4 and Claude are trained on vast amounts of public internet data. They are remarkably capable at general tasks, but they have two fundamental limitations for business use:

They do not know your data — your internal policies, project records, client details, pricing, procedures — none of this exists in their training data
They confidently make things up — when asked about something they do not know, LLMs do not say "I don't know." They generate plausible-sounding but fabricated answers. This is hallucination.

RAG fixes both problems. Instead of asking the LLM to answer from memory, you first search your own documents for relevant information, then give that information to the LLM along with the question. The model generates its answer based on your actual data, with citations pointing to the source documents.

The result: an AI system that answers questions about your business accurately, cites its sources, and says "I don't have information on that" when the answer is not in your documents.

How does a RAG system actually work?

The RAG pipeline has five main stages. Understanding these helps you make informed decisions about implementation.

1. Document ingestion

Your documents — PDFs, Word files, web pages, Confluence wikis, SharePoint sites, emails, spreadsheets — are loaded into the system. This can be a one-time bulk import or an ongoing sync that keeps the knowledge base current.

2. Chunking

Documents are split into smaller, meaningful pieces (chunks). This is more important than it sounds — chunk too large and the retrieval becomes imprecise; chunk too small and you lose context. Typical chunk sizes are 200-500 tokens, with overlap between chunks to maintain context. The chunking strategy should match your content type: paragraph-level for policies, section-level for technical documents, row-level for structured data.

3. Embedding

Each chunk is converted into a numerical representation (vector) that captures its semantic meaning. This is done using an embedding model. When you later search for information, your query is embedded the same way, and the system finds chunks whose vectors are mathematically similar to your query's vector. This means the search understands meaning, not just keywords — searching for "staff leave policy" will find chunks about "employee annual leave entitlements" even if those exact words do not appear.

4. Vector storage

The embedded chunks are stored in a vector database — a specialised database optimised for similarity search. When a query comes in, the vector database finds the most relevant chunks in milliseconds, even across millions of documents.

5. Retrieval and generation

When a user asks a question: the query is embedded, the vector database retrieves the most relevant chunks (typically 3-10), those chunks are passed to the LLM along with the question, and the LLM generates a response grounded in the retrieved content. The response includes citations pointing back to the source documents.

RAG Stage	What Happens	Key Decision
Ingestion	Documents are loaded and processed	Which documents to include, sync frequency
Chunking	Documents split into searchable pieces	Chunk size, overlap, strategy per content type
Embedding	Chunks converted to vector representations	Which embedding model (accuracy vs cost vs speed)
Storage	Vectors stored for fast retrieval	Vector database choice, hosting location
Generation	LLM produces answer from retrieved context	Which LLM, prompt design, citation format

What are the main business use cases for RAG?

RAG is not a solution looking for a problem. Here are the use cases where it delivers the most value:

Internal knowledge bases and policy Q&A

Employees ask questions about HR policies, IT procedures, compliance requirements, or project documentation and get accurate, cited answers instantly. This replaces the cycle of searching SharePoint, asking colleagues, or waiting for HR to respond. Organisations with 100+ policy documents see particularly strong adoption.

Customer support knowledge systems

Support teams use RAG to answer customer questions using product documentation, past tickets, and knowledge base articles. The system drafts responses grounded in actual product specs and troubleshooting guides, reducing average handling time by 30-50%.

Legal document search and analysis

Law firms and legal departments use RAG to search across thousands of contracts, precedents, and regulatory documents. Instead of manual review, lawyers ask natural language questions and get relevant passages with citations. For more on this, see our article on AI for legal firms.

Technical documentation and engineering

Engineering teams query manuals, specifications, standards, and past project reports. RAG handles the dense, technical language that general search tools struggle with, finding relevant information across thousands of pages of technical documentation.

What are the key technical decisions when building a RAG system?

Choosing an embedding model

The embedding model determines how well the system understands meaning. Key options in 2026:

OpenAI text-embedding-3-large — strong general performance, 3,072 dimensions, $0.13 USD per million tokens. Good default choice.
Cohere embed-v3 — competitive accuracy, supports 100+ languages, good for multilingual use cases
Open-source (e.g., BGE, E5) — can be self-hosted for data sovereignty, no per-token cost, but requires infrastructure

Choosing a vector database

Your choice depends on scale, budget, and hosting requirements:

Pinecone — fully managed, easy to set up, good for most business use cases (up to tens of millions of vectors)
Weaviate — open-source option, can be self-hosted in Australia for data sovereignty, strong hybrid search
PostgreSQL with pgvector — if you already use Postgres, this adds vector search without a new database. Good for smaller collections (under 1 million chunks)
Qdrant — high-performance open-source option, Rust-based, excellent for large-scale deployments

Cloud vs on-premise

For most Australian businesses, cloud hosting on Australian regions (AWS ap-southeast-2, Azure Australia East) provides the best balance of performance, cost, and data sovereignty. On-premise is worth considering for organisations with strict data classification requirements (defence, certain government agencies) or those processing extremely sensitive data.

Is RAG the right solution for your business?

RAG is powerful, but it is not always the right approach. Use this decision framework:

RAG is a good fit when:

You have a substantial body of documents (50+ pages) that people need to query
The information changes regularly and the AI needs to stay current
Accuracy and citations are critical (compliance, legal, customer-facing)
Users are asking varied, natural language questions (not just keyword searches)
You need the AI to say "I don't know" when the answer is not in your data

RAG may not be the best fit when:

Your knowledge base is small and changes rarely (fine-tuning might be simpler)
You only need the AI for general tasks that do not require your specific data
Your data is primarily structured and tabular (SQL or traditional analytics may be better)
You need real-time data from live systems (RAG works best with document-style content)

For guidance on choosing the right AI model to power your RAG system, see our comparison of Claude, GPT, and Gemini for business use. And if you are ready to explore building a RAG system for your organisation, take a look at our RAG system development services.