RAG integration
How retrieval-augmented generation connects AI responses to selected source material before the model generates output.
RAG and knowledge
AI answers improve when the right knowledge is retrieved, limited, and traceable.
Retrieval-augmented generation, often called RAG, connects AI output to approved documents, records, knowledge bases, and source material. Good RAG integration is not only about search. It also needs ingestion, metadata, access controls, grounding, source freshness, and auditability.
What this section explains
These guides cover the knowledge layer around AI integration: RAG, vector databases, enterprise knowledge grounding, document ingestion, and access-aware retrieval.
Vector databases
How vector search can help find semantically related passages, documents, records, or knowledge chunks.
Grounding
How approved sources, references, and source constraints help reduce unsupported answers.
Document ingestion
How documents move from files and pages into indexes, chunks, metadata, and retrieval workflows.
Access controls
How AI knowledge retrieval should respect user roles, source permissions, sensitivity labels, and data boundaries.
RAG and knowledge article list
This section contains five launch articles. Build these before treating the section as complete.
Start here
Reading order
RAG Integration Explained
Learn how retrieval-augmented generation connects AI output to approved source material, metadata, and source-aware review.
Retrieval layerVector Databases in AI Integration
Understand what vector databases do, where they fit, and why vector search is only one part of a complete RAG system.
Source groundingGrounding AI with Enterprise Knowledge
See how approved knowledge sources, citations, source references, and review rules can keep AI output closer to real organizational information.
IngestionDocument Ingestion for AI Systems
Learn how files, pages, policies, manuals, records, and knowledge material are prepared for AI retrieval.
PermissionsKnowledge Access Controls for AI
Understand why AI should only retrieve and summarize sources that the user, role, workflow, or connector is allowed to use.
Recommended path
Start with RAG integration, then vector databases, grounding, document ingestion, and knowledge access controls. That follows the path from concept to source governance.
How RAG fits into AI integration
RAG sits between knowledge sources and model output. It helps the AI use selected information instead of relying only on the model’s general training.
1
Knowledge source
Documents, records, manuals, help articles, policies, reports, or approved web pages are selected.
2
Ingestion and indexing
Source material is cleaned, chunked, labelled, indexed, and connected to metadata.
3
Retrieval
The system retrieves relevant source passages based on the user request and permission rules.
4
Grounded output
The model generates an answer using retrieved context, while logs and source references support review.
Integration reminder: RAG is not just “upload documents to AI.” It is a source,
indexing, retrieval, permission, logging, and review pattern.
Why knowledge integration matters
Many AI systems can produce fluent answers without knowing the organization’s current policies, products, support rules, procedures, contracts, documentation, or internal terminology. RAG and knowledge integration help connect AI to selected information that is relevant to the task.
But connecting AI to knowledge is not automatically safe or accurate. The system needs to know which sources are current, which sources are approved, which users can access them, which passages were retrieved, and what happens when sources disagree.
| Knowledge concern | Plain meaning | Why it matters |
|---|---|---|
| Source selection | Which documents, records, or pages are allowed into the retrieval system. | Bad sources can produce bad answers. |
| Freshness | Whether the source is current, archived, draft, deprecated, or superseded. | Old source material may contradict current policy or operations. |
| Metadata | Labels such as title, owner, date, system, sensitivity, version, and status. | Metadata helps retrieval, review, filtering, and audit trails. |
| Permissions | Who is allowed to retrieve or summarize each source. | AI should not reveal restricted material through summaries. |
| Source evidence | References showing what the AI used. | Users and reviewers can check whether the answer is grounded. |
Questions before building a RAG system
- Which sources are approved for retrieval?
- Who owns each source collection?
- How are old, draft, duplicate, or superseded documents handled?
- What metadata is attached to each document or chunk?
- Does retrieval respect user and role permissions?
- Can users see which sources shaped an answer?
- What happens when sources disagree?
- How are bad answers, missing sources, and retrieval failures reviewed?
How this section connects to the rest of the site
RAG and knowledge integration depend on many other parts of the AI stack. Data systems provide records and metadata. APIs and connectors move source material. Identity rules control access. Model platforms serve the final request. Monitoring watches retrieval failures and quality issues.
Data Systems
Source quality, metadata, and lineage affect how useful retrieved knowledge will be.
APIs and Connectors
Connectors often bring documents, records, and knowledge sources into retrieval workflows.
Identity and Access
Knowledge retrieval must respect roles, permissions, and service-account boundaries.
Monitoring and Observability
Retrieval quality, source freshness, missing references, and user corrections need review signals.
Educational limitation
This section provides general educational information about RAG and knowledge integration. It is not legal, financial, medical, engineering, safety, cybersecurity, procurement, compliance, privacy, or professional advice. It does not provide instructions for bypassing controls, exploiting systems, unauthorized access, or unsafe automation. Use qualified review before connecting AI retrieval systems to sensitive data, regulated systems, production infrastructure, customer records, financial processes, safety systems, or other high-consequence environments.