You're identifying and managing risks when using large language models—hallucination, privacy, reliability, and latency issues.
This evaluates what can go wrong before anything is built.
Step 1: Hallucination Risk
Ask:
- Does the task require precise facts?
- Would a wrong answer cause confusion or harm?
- Is there a single correct answer?
Low-risk example: Drafting internal brainstorming notes.
High-risk example: Providing policy guidance to customers.
Flag high-risk tasks clearly.
Step 2: Privacy and Data Risk
Determine:
- Whether personal or confidential data is involved
- Who is allowed to see the data
- Whether data leaves internal systems
If sensitive data is involved, require safeguards or exclude AI use.
Step 3: Reliability Risk
Ask:
- Does this task need consistent output every time?
- Would variability cause problems?
- Can humans easily review the output?
Tasks requiring strict consistency carry higher risk.
Step 4: Latency and Availability Risk
Consider:
- How fast the output is needed
- Whether delays block other work
- What happens if the system is unavailable
Time-critical tasks increase risk.
Step 5: Human Oversight Requirement
Decide:
- No review needed
- Light review required
- Mandatory human approval
Human-in-the-loop lowers risk.
Step 6: Assign Risk Level
Label each task as:
- Low Risk
- Medium Risk
- High Risk
Add notes explaining why.
What You Should Have Now
✅ LLM Risk Assessment List
✅ Risk level per task
✅ Required safeguards and oversight notes
Quality Check
- Risks are clearly explained
- High-risk tasks are not hidden
- Safeguards match risk level
- No task proceeds without review rules
Next Step: With risks assessed, you're ready to score opportunities by difficulty.
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