Knowledge Management
How content enters AskRAI, gets organized into knowledge packs, and powers AI responses through Text Search and AI Search
The knowledge base is the single source of truth for your AI assistant. Every response is grounded in content your team put there, rather than in the model's own training data, and nothing outside it is retrieved. To see exactly which items a question retrieved, replay it in the Sandbox — the retrieval set is not kept in the audit record.
Two content types
AskRAI supports two types of knowledge content, each suited to different use cases:
| Type | What it is | Best for |
|---|---|---|
| Q&A pairs | A question and its answer — written by a curator, imported from a CSV or XLSX table, or generated from a document you upload | Direct answers to common questions (Resolve, Inform) |
| Document chunks | Segments extracted from uploaded PDFs | Reference material, policies, and detailed procedures (Inform, Support) |
Both types live in the same knowledge base. The difference is in how they enter the system — and in how they are read: admin AI Search merges both, while at query time the assistant searches each separately and keeps whichever leg scored higher, summarizing that one alone.
Content ingestion pipeline
When you upload a document to a knowledge pack, AskRAI processes it through an automated pipeline.
- Upload — drag and drop files into a document-type knowledge pack. A document-type pack accepts PDF only; a Q&A-type pack also accepts CSV and XLSX tables of ready-made pairs. There is no OCR, so a PDF has to carry a real text layer — a scan or a photograph fails.
- Extract — the system reads the file's text layer, keeping track of which page each passage came from.
- Chunk — long documents are split into overlapping windows that each capture a coherent unit of information.
- Embed and store — each chunk is turned into a vector and written to the knowledge base, which makes it searchable the moment it lands.
For Q&A pairs, you write the question and answer directly — no parsing pipeline needed.
A new Q&A is not live when you save it. It is created as a Draft, and only Approved items are retrievable — the assistant cannot see a draft. To publish a Q&A you must approve it, and that takes effect immediately: there is no indexing run to wait for. If a curator adds an answer and users still get "I don't know", an unapproved draft is the first thing to check.
Knowledge Packs
Knowledge packs are containers that group related content together. Each pack has a content type (Q&A or Document) chosen at creation, and content within a pack shares the same lifecycle and access controls.
Packs serve two purposes:
- Organization — group content by topic, department, or use case (e.g., "HR Policies", "Building Permits", "IT Self-Service")
- Access control — groups reference knowledge packs to control which users can access which content
A user's AI assistant only searches knowledge packs that their group memberships grant access to. Content in packs outside their groups is invisible to their queries.
Content lifecycle
Every knowledge item follows a status workflow that controls whether it appears in search results:
| Status | Visible to users? | Description |
|---|---|---|
| Draft | No | The starting status for a hand-written Q&A — awaiting review |
| Approved | Yes | Reviewed and live — appears in search results |
| Needs Attention | No | Flagged for review — temporarily hidden |
| Discarded | No | Soft-deleted — excluded from all searches |
Only Approved content is retrievable, so an item in any other status answers nothing.
What you upload decides whether anyone reviews it. Chunks produced for a Document pack are created Approved and are live as soon as processing finishes — uploading a document to one publishes its content with no review step. Everything bound for a Q&A pack lands as Draft instead and answers nothing until approved: a pair you write by hand, a CSV or XLSX table you import, and the pairs generated from a PDF uploaded to a Q&A pack. So importing five hundred pairs and then asking about them returns nothing until you approve them — use Bulk Approve All QnAs on the pack once you have read them.
Two search modes
AskRAI offers two ways to search the knowledge base:
- Text Search — substring matching against questions, answers, titles, section headings, document content and tags. Fast and predictable, and the only mode that reliably finds an exact identifier or phrase.
- AI Search (semantic) — meaning-based search that finds relevant content even when the wording differs from the query. Uses vector embeddings to match by concept rather than keywords.
These two modes are a curator tool on the Knowledge Base page — they are how you look content up. They are not a runtime setting. At query time the assistant always searches by meaning, the way AI Search does; there is no per-tenant switch that selects a retrieval mode, and nothing to configure.
Next steps
- Knowledge Base — create and manage Q&A pairs and document chunks
- Knowledge Packs — organize content into collections and upload files
- Guardrails — understand how safety rules protect knowledge-based responses
- Access Control — learn how groups control access to knowledge packs