When a general chatbot is not enough
Public model knowledge cannot tell your employees which internal policy is current or who is allowed to read a particular document. A knowledge assistant connects the question to relevant organisational sources before drafting an answer. Retrieval quality and permissions matter as much as the model.
- Internal policies and operational guidance.
- Product documentation and support knowledge.
- Research collections and approved reference material.
What goes into a useful knowledge system
We review the source collection, document ownership and update process. The implementation then needs ingestion, retrieval, answer generation and evaluation. If the source does not support an answer, the assistant should say so or direct the user to the right team.
- Source inventory and document preparation.
- Search and retrieval design, including Arabic and English testing.
- Permission-aware access and source citations.
- Evaluation questions, update routines and handover guidance.
A practical bilingual example
A team may use Arabic procedures alongside English supplier manuals. An employee should be able to ask a question in either language and see which approved source supports the response. We would test whether terminology, scanned pages and tables are retrieved accurately. This is an illustrative use case.
- Test retrieval before judging the generated answer.
- Separate outdated documents from current guidance.
- Verify that restricted sources stay restricted.
RAG, fine-tuning or a simpler search?
RAG is useful when answers depend on changing source material. Fine-tuning may help with a repeated behaviour or output pattern, but is not automatically a replacement for retrieval. Sometimes a well-designed search interface is enough. The requirement should determine the architecture.
- Choose the smallest useful solution.
- Evaluate unsupported answers and missing evidence.
- Plan how sources will be maintained after launch.
Before we begin.
Does RAG eliminate incorrect answers?
No. Retrieval can improve grounding, but the system still needs evaluation, clear limits and appropriate review. Source citations also need to be checked for relevance.
Do we need to train a model on all our documents?
Not necessarily. A retrieval-based system can find relevant passages at query time. We assess that approach before considering model training.
Can we choose where the solution is hosted?
Deployment options depend on your security requirements, model availability, integrations and budget. These constraints are established before the architecture is chosen.