Summaries that keep the signal

Summaries that keep the signal

Why it matters: Good summaries preserve what matters and are easy to act on.

Goals

• Summarize by purpose (not by length).
• Preserve key constraints, decisions, and risks.
• Produce an action list.

Key definitions

• Signal: The core meaning: decisions, constraints, reasons, and actions.
• Noise: Details that do not change decisions or actions.
• Compression: Reducing length while preserving signal.

Workflow (step-by-step)

1. Choose a summary purpose: brief, decision memo, action plan, update.
2. Extract: decisions, constraints, numbers, risks, and open questions.
3. Write a 5–10 bullet “signal” summary.
4. Add a “what’s missing” section (unknowns).
5. Add actions with owners and dates (when applicable).

Example (good vs bad)

✅ Good: A summary that includes decisions + risks + next actions.

❌ Bad: A summary that rewrites the text with fewer words but no structure or actions.

Checklist

• Purpose stated.
• Decisions/constraints preserved.
• Actions included.
• Unknowns flagged.

Metrics / criteria

• Read time < 2 minutes; action list usable.
• 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: Summarizing without purpose. Fix: Define audience + decision context first.
• 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 summaries that keep the signal.
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/