A local LLM runs inference on a chosen computer or server. Locating the model near data helps define processing paths, but OCR, embedding and log services still need separate review.
Putting it into practice
Model size is not the only criterion. Test Thai, document tables and real questions. Measure concurrency, memory, latency and operating cost. Plan model updates and rollback if quality falls.
Before you begin
- Language quality
- Concurrent users
- Updates and rollback
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.