璟知科技
璟知科技
JINGMIND AI
RAG Knowledge Base

RAG Knowledge BaseTurn company materials into searchable and citable knowledge assets

Many companies have knowledge scattered across drives, chats, sheets, personal computers, and project documents. A RAG knowledge base helps employees answer, retrieve, draft, and decide based on current, trusted, and source-cited materials.

When does a RAG knowledge base fit?

  • Project materials are scattered and new employees cannot find answers.
  • Policies, manuals, SOPs, or product materials change often and answers need sources.
  • Consulting, research, and training teams want to reuse reports, cases, and templates.
  • Manufacturing or operations teams need to preserve manuals, quality standards, and experience.
  • The company wants to validate value with maintainable knowledge before fine-tuning models.

Knowledge base deliverables

  • Knowledge architecture defining categories, tags, permissions, and owners.
  • Document governance and ingestion with sanitization, chunking, indexing, and version marks.
  • Semantic search and Q&A with natural language questions, follow-ups, and source citations.
  • Permission and safety rules controlling which teams can access which materials.
  • Update and evaluation mechanism for knowledge refresh, answer sampling, and admin handoff.

Process

1

Material inventory

Review documents, sheets, manuals, cases, and historical records.

2

Architecture design

Define categories, tags, permissions, citation format, and Q&A boundaries.

3

Ingestion testing

Index the first batch and test retrieval quality, citations, and error boundaries.

4

Handoff operation

Train admins and establish update, quality sampling, and iteration routines.

FAQ

Start with one valuable set of materials and build a searchable, citable, maintainable knowledge base.

Discuss knowledge base scenario

How is RAG different from fine-tuning?

RAG lets the model answer with company materials and is better when knowledge changes often or sources are required. Fine-tuning is better for stable style, classification, or fixed output habits.

Can we start if materials are messy?

Yes, but not without governance. Start small and define versions, permissions, categories, and non-citable content first.

Who maintains the knowledge base after launch?

A business owner or admin must be assigned. We deliver update rules, admin handoff, and answer evaluation methods.