Find the evidence you need as it changes, from equipment data to new regulations, patents and work documents. Help AI learn your proven writing, classification and review patterns, so you don’t have to explain them from scratch every time.
Providing information alone doesn’t teach AI how your organization works.
The problem isn’t how much information you have.
Evidence to retrieve and standards to learn need different approaches.
RAG and fine-tuning solve different problems. They can work together in the same workflow.
Search relevant passages and locations in sources the user can access, without retraining on each new document.
Use proven examples and revision history to teach the model recurring structures, classification rules and writing patterns.
Connect the evidence and your organization’s standards to review, approval and storage.
Manage information and permissions in the workspace, and inspect RAG indexing alongside fine-tuning preparation, training and evaluation.
Review document and embedding status, and incorporate new information without retraining.
Convert XML, HTML, JSON and TXT in one place, then connect the results to datasets for pretraining or supervised fine-tuning.
Each industry needs different evidence and recurring judgment patterns. Decide what to retrieve with RAG and what to learn through fine-tuning, task by task.
| Industry | Retrieved with RAG | Learned through fine-tuning | Decided by people |
|---|---|---|---|
Legal and patentsProfessional documents with traceable evidence | Current legislation, case law, patents and case files | Pleading structure, claim comparison tables and review style | Legal strategy, patentability and final submission |
Manufacturing and equipmentFrom field records to actionable reports | Manuals, inspection history, error codes and equipment documents | Anomaly categories, action reports and field terminology | Root cause confirmation, maintenance and operating decisions |
Healthcare and researchKeep source records and study formats connected | Source records, protocols, study documents and supporting papers | CRF narratives, classification criteria and review templates | Medical judgment, participant safety and study approval |
Finance and insuranceConnect regulations with organizational review criteria | Product terms, regulations and customer submissions | Review items, statements of reasons and risk classification formats | Approval, rejection, exceptions and risk decisions |
Education and contentCurrent information. A consistent voice. | Textbooks, course material and current references | Feedback style, rubrics and content structure | Learning goals, assessment and publishing decisions |
AI doesn’t make the final call. It prepares a draft combining evidence and organizational standards for experts to review and decide.
Invention disclosures, specifications, drawings and claims
KIPRIS candidates, publication and registration details, and prior-art sources
Claim element breakdowns and review table structure
Documents, elements and follow-up research for expert review
Patent attorneys and relevant experts assess novelty, inventive step, infringement and final submission.
Start with RAG when the goal is to find and answer from current documents. Consider fine-tuning when you have enough validated examples and need to apply the same standards repeatedly.
Pick one recurring task whose results you can evaluate.
Distinguish current knowledge to retrieve from good examples to learn.
Record the current model’s errors and expert revision time.
Use the same evaluation set to compare format, omissions and evidence.
Start within expert-approved boundaries and collect reviewed corrections.
Review your documents and strong examples, then identify what RAG can solve, where fine-tuning may help and where expert approval belongs.
