Pros/cons and decision support

Pros/cons and decision support

Why it matters: Decision support should reveal tradeoffs and assumptions, not just list pros/cons.

Goals

• Write a decision table.
• Surface assumptions and risks.
• Pick a recommendation with conditions.

Key definitions

• Tradeoff: A gain in one dimension that costs you in another.
• Assumption: A belief that must be tested for the decision to hold.
• Recommendation: A choice plus the conditions under which it’s valid.

Workflow (step-by-step)

1. List options (2–4).
2. List criteria (3–6) and define what “good” means per criterion.
3. Write pros/cons tied to criteria (not generic).
4. List assumptions and risks per option.
5. Recommend one option and state conditions + next test.

Example (good vs bad)

✅ Good: A recommendation that includes conditions, risks, and a next test.

❌ Bad: A generic pros/cons list with no criteria or decision.

Checklist

• Criteria defined.
• Tradeoffs explicit.
• Assumptions listed.
• Recommendation includes conditions.

Metrics / criteria

• Decision made within timebox; next test scheduled.
• Measurable: You can say whether the output is correct/complete (pass/fail or a score).
• Effort: You can produce the result in 15–30 minutes using the workflow.
• Clarity: Another person can run your prompt and get a similar outcome.

Common mistakes + fixes

• Pitfall: Pretending all criteria are equal. Fix: Weight criteria or define a priority order.
• Pitfall: Vague instructions. Fix: Add constraints, examples, and a success checklist.
• Pitfall: One-shot prompting. Fix: Iterate: draft → critique → revise → verify.
• Pitfall: No validation. Fix: Add QA steps, tests, and explicit “what counts as done”.

Student tasks

1. Apply the workflow to one real work task related to pros/cons and decision support.
2. Create a small artifact: prompt, checklist, rubric, table, draft, or decision note.
3. Run one quality check and record what changed after the check.
4. Write a 3-sentence reflection: what worked, what failed, and what you will reuse.

Useful external sources

• OpenAI prompt engineering guide: https://platform.openai.com/docs/guides/prompt-engineering — Prompt structure, iteration, constraints, and evaluation habits.
• NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework — Risk framing, governance language, and verification mindset.
• OWASP Top 10 for Large Language Model Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/ — Practical AI safety risks such as prompt injection, leakage, and insecure output handling.
• Google People + AI Guidebook: https://pair.withgoogle.com/guidebook/ — Human-centered AI design, user control, feedback, and failure modes.

Bibliography

• OpenAI prompt engineering guide. https://platform.openai.com/docs/guides/prompt-engineering
• NIST AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework
• OWASP Top 10 for Large Language Model Applications. https://owasp.org/www-project-top-10-for-large-language-model-applications/
• Google People + AI Guidebook. https://pair.withgoogle.com/guidebook/