RAG examples and patterns
RAG examples that connect architecture to business work
See where retrieval-augmented generation adds value, which sources and controls each use case needs, and how to define a measurable first release.
Source-connected
Permission-aware
Measured before launch
01
What makes a strong RAG use case
RAG is a good fit when users need answers or drafts based on information that is private, specialized, distributed, frequently updated, or important to cite. The workflow should have identifiable source material and a way to judge whether retrieval and the final response are useful.
RAG is a weak fit when no authoritative knowledge exists, deterministic rules are sufficient, the task primarily requires model behavior rather than facts, or an incorrect synthesis would be unacceptable without a review process.
02
Enterprise RAG examples
Knowledge assistant
Search policies, procedures, project history, and expert guidance with source-aware answers.
Customer support copilot
Combine product documentation, customer context, and similar resolved cases for faster service.
Document analyst
Find, compare, and summarize relevant requirements, clauses, or technical passages across a controlled set.
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Industry patterns
CRM account briefings and case-resolution guidance
Legal matter, contract, and precedent knowledge
Manufacturing manuals, service history, and quality records
Healthcare policy, research, and operational knowledge
Sales proposal and approved-response assistance
Compliance obligations and evidence retrieval
Engineering specifications and change history
Employee onboarding and self-service support
04
Turn an example into a testable pilot
Name the user decision
Describe the task and what better performance means.
Identify authoritative evidence
Select representative sources, versions, permissions, and expected citations.
Create realistic questions
Include frequent, difficult, ambiguous, restricted, and unanswerable cases.
Measure against today
Compare time, retrieval, answer support, review effort, and user outcomes.
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Go deeper by use case
Questions
Frequently asked questions
A practical next step
Bring us the workflow that is stuck—not a finished AI specification.
We will help clarify the opportunity, data, risks, and smallest useful way to prove value.