Implementing RAG: Effort, Costs and Pitfalls from Practice
We already explained what Retrieval-Augmented Generation is in our RAG introduction here on the blog. In short: the AI answers questions not from gut feeling but from your own documents, and it shows where each answer comes from. This article is about the next step, introducing it in your own company. What is coming your way, what should you plan for and where does it typically get tedious?
How a RAG rollout works in practice
- Review your data sources: Where does the knowledge actually live? It is usually spread across file shares, wikis, emails and PDFs, and part of it exists only in the heads of individual employees.
- Pilot with a clear knowledge domain: Ideally you start with a topic that gets asked about often and is well documented. A pilot does not have to cover the whole company.
- Check quality with real questions: Not with made-up test questions but with what actually gets asked day to day. Only when the answers work there is the system any good.
- Expand step by step: Only once the pilot convinces do further knowledge areas follow.
What effort is realistic?
A RAG pilot is a matter of weeks, not a year-long project. The biggest effort rarely lies in the technology but in reviewing and preparing your own documents. If your documentation is reasonably in order, you are ready to start quickly.
One point is easily forgotten: a RAG system is not something you set up once and tick off. New documents arrive, old ones go stale. Ongoing maintenance needs to be planned from the start, otherwise answer quality slowly degrades.
The typical pitfalls
- Outdated and contradictory documents: If three versions of a manual are in circulation, the AI will eventually cite the wrong one. Cleaning up before ingestion pays off.
- Access rights: Not everyone may see everything. Salary data and HR files do not belong in a knowledge base for the whole workforce.
- Inflated expectations: RAG answers questions about what is documented. What is written down nowhere, even the best AI cannot know.
- Missing ownership: Without a clear owner for maintenance the content goes stale, and with it the employees' trust in the answers.
And data protection?
For many businesses the most important question. A RAG system does not have to run with a US provider. Operation with EU hosting or on your own premises is possible, and access rights can be modelled so that everyone only receives the answers they are allowed to see. This belongs in the concept before the first file is ingested.
Simply try it out
The fastest way to understand RAG is to use it. In our live demo you ask questions about the emails of an employee who has left and see immediately what sourced answers look like. And if you want to know what this would mean for your business, we are happy to look at it together.