How to plan a RAG pilot for business knowledge
A practical guide to choosing documents, designing retrieval and measuring a grounded AI assistant for your business.
When is RAG useful?
Retrieval-augmented generation (RAG) searches an approved knowledge collection and provides relevant excerpts to a language model. It can help with changing policy documents or product information where an answer should point back to evidence.
RAG is not a substitute for reliable source material or access controls. If the answer is absent, the assistant should say so instead of inventing it.
Build a useful baseline
Choose one collection and record document ownership, update frequency, formats and permissions. Digital PDFs and scanned pages may require different extraction and OCR paths. Split content around meaningful boundaries and retain page and document references.
Create representative questions with expected sources. Compare search results before adding generation, then review generated answers against their evidence.
- Include exact identifiers and paraphrased questions in the test set.
- Test conflicting, missing and restricted documents.
- Measure retrieval quality, grounded answers, latency and operating cost.
Plan for operation
Decide how documents are updated, who can see each source and how failures are investigated. Version the embedding model and index; changing an embedding model generally requires re-embedding the corpus and rebuilding the corresponding index.
BuildrLab can scope a pilot with an agreed collection, interface, evaluation and handover. Explore the RAG Field Guide for a runnable example.
Have a project in mind?
Explore BuildrLab services or tell us what you want to achieve.