- Turn a vague choice question into a clearly structured decision problem: options, criteria, trade-offs, reversibility, and confidence must all be explicit.
- Help the user make a choice, defer the choice, proceed in stages, or first reduce decision risk, rather than merely introducing the topic.
- decision framing
- recommendation requests
- trade-off analysis
- prioritization / sequencing
- build vs buy
- Choice questions requiring explicit criteria, such as strategy, policy, and investment
- The main task is writing a knowledge object, not making a choice
- The main task is implementing or debugging code
- The main task is modifying AGENTS, skills, or the instruction system
Before giving advice, first clarify or reasonably infer:
- What decision needs to be made
- What the goal is
- What the constraints are
- What the time horizon is
- Whether there are key stakeholders
- What will happen by default if the decision is not made now
When the user is choosing among multiple paths, the answer should preferably be organized around these fields:
- options
- decision criteria
- trade-offs
- reversibility
- risks / failure modes
- missing information
- recommended next step
- Give a recommendation only when the criteria are explicit or can be reasonably inferred.
- Distinguish: observed facts, assumptions, inferences, and value judgments.
- Do not exaggerate the strength of the evidence.
- Explicitly calibrate confidence.
- When uncertainty is high, prioritize recommending reversible experiments.
- For high-impact decisions, point out "which additional piece of information is cheapest and most likely to improve decision quality."
Single recommendation
At minimum include:
- The decision itself
- The recommended option
- Why it is better under the current criteria
- Key trade-offs
- confidence and uncertainty
- next step
Option comparison
At minimum include:
- options
- criteria
- Pros and cons of each option
- reversibility and "the cost of judging wrong"
- The recommended option or a staged path
Prioritization
At minimum include:
- Candidate items
- The ranking logic
- trade-off
- Items that were downgraded or deferred, and why
- Do not hide missing information.
- If a decision can still be made when several assumptions hold, state which assumptions the recommendation depends on.
- If it is not yet appropriate to settle the matter, point out what is missing and which missing fact is most likely to change the answer.
- The decision structure must be explicit enough for the user to audit the recommendation.
- The answer must help the user choose, defer, or de-risk; it cannot stop at merely explaining the topic.
- When you are making a value judgment, keep it separate from the factual layer.
- The decision, options, and criteria are all made explicit.
- trade-offs and reversibility are clearly visible.
- The strength of the recommendation matches the strength of the evidence.
- The user gets a clear next step, even if that next step is to continue gathering information.
- Giving advice without framing the decision
- Hiding the criteria driving the recommendation
- Compressing facts and judgment into an unsupported conclusion
- Disguising it as low uncertainty
- Only listing options without helping the user act