Start with one real case for Grounding and RAG. Record the start and the goal.
Public information should stay clear and consistent. Check copy, sources and data.
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Grounding and RAG: Common mistakes to avoid
This guide covers Grounding and RAG. Use one real case and keep the next step clear.
What matters in practice
Key rule: Separate external facts from model knowledge and use traceable source context for important answers.
Look for clear mistakes first. Then change one point only.
A reliable way to work
Set one question before the test. Decide what result should count.
Name one owner. Record the current state.
Change one point only. Then check the result.
If money, customers or public claims are involved, add a human review.
Check the result again later. Keep only what helps the goal.
Set the goal
- Use one real case.
- Record the current state.
- Name the person who owns the task.
- Keep the first test small.
Check the starting point
- Write down open points.
- Keep the source easy to find.
- Mark unclear data before the change.
- Check one risk at a time.
Change one point
- Let a person review important claims.
- Keep the test easy to repeat.
- Use the same measure as before.
- Record the result right away.
Secure the result
- Share the result with the right person.
- Keep open points visible.
- End the test when the goal is met.
- Use the result in the next decision.
Practical checklist
- Write the goal in one sentence.
- Record the start and one real case.
- Check the key rule.
- Name the owner and approval point.
- Set one measure and one date.
Common mistakes
- Starting without a clear goal.
- Changing several points at once.
- Using a result without a final check.
Concrete example
Example: An SME tests Grounding and RAG with one real case. The team records the start, changes one point and checks the result. Then it plans the next step.
Related guides
At a glance
Four relevant points to understand and check on this page.
What this subject covers
Grounding and RAG: Separate external facts from model knowledge and use traceable source context for important answers.
The rule for this area
AI systems need clear public content, distinct entities and verifiable sources. Extra files do not replace that base.
What this perspective checks
Look first for mistakes that directly reduce effectiveness, data quality or trust.
Check it in practice
Use one real case for Grounding and RAG. Record which rule you tested and whether the result meets the goal.