An AI knowledge base prepares content for retrieval and context: documents, extracted text, permissions, owners and versions. Uploading files alone does not make knowledge accurate or ready.
Putting it into practice
Start with permitted PDF, DOCX, XLSX, PPTX, TXT and Markdown. Validate tables and OCR against real files. Set review cycles and retirement rules. Web, database and API sources need connector and source-rights review.
Before you begin
- Document owner
- Active version
- OCR and table quality
Capabilities and deployment require project-level confirmation. No unsupported certification, ROI or customer claims are made.
Frequently asked questions
Does data have to leave the network?
It depends on deployment and connected services. Map model, OCR, embedding, backup and log traffic before confirming the boundary.
Can an AI answer be trusted immediately?
Check the original, completeness and version, especially for legal, accounting and consequential decisions. Citations support review but do not guarantee accuracy.
How should a project begin?
Choose one defined use case, approved documents, owners and acceptance criteria. Test answer quality, permissions and cost with a small group before scaling.
References
Public sources explain principles; they do not certify or endorse RAGBOX.
- NIST AI Risk Management Framework
- Microsoft: Retrieval-augmented generation
- OWASP: RAG Security Cheat Sheet
- OWASP: Prompt Injection Prevention
Expert review is required before operational use. This is not case-specific legal or tax advice.