When retrieval-augmented generation is useful
When useful knowledge is spread across PDFs, manuals, policies, contracts or SharePoint folders.
RAG defined for business
RAG is an AI pattern that retrieves relevant source material before generating an answer, so the response can be grounded in known documents.
What it means
How RAG affects Australian small and medium businesses.
It is useful when staff need answers from policies, manuals, contracts, procedures or product notes and need to see where the answer came from.
Related service
AI model integrations inside websites, CRMs, portals, dashboards and internal tools. We work across major model providers including GPT and OpenAI, Anthropic Claude, Google Gemini, Microsoft Azure AI, Meta Llama, Mistral, Cohere, DeepSeek, xAI Grok and Amazon Bedrock-hosted models, with prompts, retrieval, guardrails, logging and human review handled properly.
Open AI IntegrationWhen it helps
Where it helps, what to check first and a practical Australian business example.
Before choosing retrieval-augmented generation
RAG still needs source quality, permissions and testing. Bad documents produce bad answers.
Retrieval-augmented generation in practice
A training provider might use RAG so staff can ask policy questions and see the source document behind each answer.
AI integration connects a model to an existing workflow. If the task is still unclear, start with AI consulting. If the surrounding software does not exist yet, the scope may also need application development. Our Good Designs work summary covers a CRM and AI engagement.
Questions
Short answers about RAG for Australian business owners.
What is RAG?
RAG is an AI pattern that retrieves relevant source material before generating an answer, so the response can be grounded in known documents.
How does RAG affect a small business?
It is useful when staff need answers from policies, manuals, contracts, procedures or product notes and need to see where the answer came from.
Can Doohickey help with RAG?
AI integration connects a suitable model to your website, CRM, portal or internal tool through a controlled API workflow. The build can use major model families and providers such as GPT and OpenAI, Anthropic Claude, Google Gemini, Microsoft Azure AI, Meta Llama, Mistral, Cohere, DeepSeek, xAI Grok or Amazon Bedrock-hosted models, with prompts, retrieval, permissions, logs, cost controls, evaluations and human review planned around the job.