How does AI access your organisation's knowledge without retraining a model whenever information changes?
Business knowledge is usually spread across documents, wikis, databases and operational systems. The key questions are not merely whether AI can access it, but which source it may use in a given situation and how an answer is supported.
how retrieval-augmented generation, or RAG, works at a high level
how standard, hybrid, multi-hop and GraphRAG differ
which sources are suitable for a knowledge assistant
why permissions, freshness and citations are essential
when search, RAG or a structured interface is the better tool
These questions connect a technical term to a concrete organisational decision.
Which sources are authoritative, current and approved for the audience?
Does the answer need a citation or an exact live value from a system?
Who owns content, permissions and quality in day-to-day operation?
Retrieval-augmented generation is an approach in which an application first retrieves relevant content from approved sources and an LLM then answers using that context.
There is no binding official list of RAG types. In practice, terms such as hybrid RAG, multi-hop RAG, GraphRAG and agentic RAG describe different properties of a system – which means they can be combined.
RAG is suitable for current, source-based knowledge from documents. Fine-tuning is suitable for recurring tasks or consistent desired behaviour learned from strong examples. A direct API is appropriate when the application needs exact live values, structured records or transactions from a business system. Many enterprise solutions combine these approaches.
An embedding is a numerical representation of content in which semantically similar items are closer in vector space. A vector database stores these vectors with content and metadata and retrieves similar entries.
Suitable business data addresses a clear user need, has an identifiable authoritative owner, is sufficiently current and understandable, and has access rights that can be carried into the application technically.
Sources, permissions and freshness remain intact when original identity and document metadata are carried through to every result, access is enforced before content reaches the model, and updates and deletions are synchronised into the index in a controlled way.
Documents and databases can be authoritative information sources. An API is usually the controlled access path to a responsible system or process. Documents suit explanatory knowledge, databases suit structured queries, and APIs suit controlled read or write operations.
When sources conflict or are missing, the application should not invent its own truth. It checks priority and validity, exposes unresolved conflicts and directs the case to a responsible person or process when evidence is insufficient.
An AI knowledge base stays reliable when source ownership, synchronisation, permissions, quality measurement, monitoring and error handling are organised as recurring operational responsibilities with clear owners.
OKF organises organisational knowledge as a directory of Markdown files with YAML metadata. Each file describes a concept such as a metric, table, API or procedure; links and optional index files connect the concepts into a portable knowledge bundle.
We translate your starting point into understandable options and a realistic next step.
