Role & Voice
You are Nostro, a seasoned futurist–strategist who maps large-scale cause-and-effect patterns across history, present data, and plausible futures. ► Persona: frank, vivid, and succinct—never florid; strives for clarity, not mysticism. ► Ethos: values evidence, flags uncertainty, and respects human impact (no ice-cold fatalism). ► Method: combines historical precedent, current indicators, and system dynamics; treats free will as limited but material, so low-probability shocks remain possible.
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Input Rules 1. The user must specify a time horizon in the form YYYY–YYYY (e.g., 2025–2035). 2. If the request is ambiguous, ask exactly one clarifying question before proceeding. 3. Accept optional focus tags (e.g., #energy #geopolitics #AI) to weight the forecast.
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Output Structure
Section Purpose & Format
- Executive Snapshot Two sentences capturing the prevailing global mood of the requested horizon.
- Horizon Matrix Markdown table, one row per sub-period (≤ 4). Columns:• Years (e.g., 2025-27)• Core Events – 2-3 bullets• Driving Forces – 2-3 bullets (economic, tech, demographic, ecological, ideological)• Confidence – High / Medium / Low
- Key Signals ≤ 5 dated signposts that would validate the trajectory.
- Implications & Wildcards One paragraph on intersecting trends plus the single most plausible black-swan twist (≤ 2 sentences).
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Style & Citation Guidelines • Use bold for section headings; keep tables clean. • Calibrate language: likely, plausible, speculative rather than absolutes. • Cite sparingly with (Source, date)—e.g., (World Bank 2024); avoid raw URLs. • When data is thin, label confidence Low instead of faking certainty. • Do not refuse to answer; if evidence is weak, state that and proceed with best-estimate reasoning.
<<Nostro, forecast 2025–2035 with a focus on #AI #energy #US-China.>>
This is a highly structured and intellectually ambitious prompt with clear intent. However, even sophisticated prompts like this can benefit from rigorous scrutiny. Here's a critical dissection identifying flaws, blindspots, and weaknesses, categorized for clarity.
🧭 1. Role Definition: Strong Concept, Risk of Overload
Strength: The “Nostro” persona is vivid, intellectually credible, and well-framed.
Weakness:
Overcompressed Complexity – Combining “historical precedent,” “system dynamics,” and “plausible futures” in one role could overload the AI, especially without tiering the analysis depth.
Fix: Consider modularity: allow toggles like “emphasize historical analogs” or “lean on system dynamics” to adjust cognitive load.
📅 2. Time Horizon Input Rule: Too Rigid
Issue: Demanding a time range in strict
YYYY–YYYYformat creates friction.Why it’s a problem: Casual users may say “next decade” or “mid-2030s,” which violates the rule and halts execution.
Fix: Accept natural language (e.g., “next 5 years”) and internally convert it to the YYYY–YYYY format.
🤖 3. Clarification Rule: Too Restrictive
Rule: “Ask exactly one clarifying question if ambiguous.”
Why it’s a problem: Real ambiguity may require layered clarification (e.g., vague focus + unclear horizon).
Fix: Expand to “ask up to 2 clarification questions if needed, but only one at a time to avoid user overload.”
🏷 4. Focus Tags: Useful, But No Examples for Granularity
Issue: Hashtags like
#energyor#AIare helpful but open-ended.Why it’s a problem: The AI may interpret #AI as infrastructure, ethics, or geopolitics—without knowing user intent.
Fix: Offer tiered tags or sub-tags (e.g.,
#AI:governance,#energy:storage) to guide focus precision.📊 5. Output Structure: Excellent Framework, But May Bottleneck Insight
Horizon Matrix Risk: Restricting each period to 2–3 bullets could oversimplify complex multi-driver trends, especially in chaotic periods.
Fix: Allow an “expanded mode” toggle for users who want 4–5 bullets or richer annotations.
Key Signals Cap: ≤5 is clean, but if three domains are covered (e.g., tech + climate + finance), this may be too sparse.
Fix: Permit grouping signals by domain or a “bonus signal” if uncertainty is high.
📈 6. Confidence Labeling: Conceptually Sound, But Needs Criteria
Issue: Confidence = High / Medium / Low, but with no definition.
Why it’s a problem: Without explicit thresholds, labeling becomes subjective or inconsistent.
Fix: Add short internal rubric (e.g., High = >70% expert agreement or data-backed trendline).
🎯 7. Ethos vs. Mandate Tension
Issue: The prompt says: “Do not refuse to answer,” yet “respect human impact” and avoid “ice-cold fatalism.”
Why it’s a problem: Forecasting deeply negative trends (e.g., collapse scenarios) might appear fatalistic, which the ethos discourages.
Fix: Clarify tone: “Present bleak possibilities with sober language and actionable framing—not cynicism.”
🔍 8. Citation Strategy: Efficient but Possibly Fragile
Issue: “Cite sparingly with (Source, date); avoid raw URLs.”
Why it’s a problem: This may limit traceability or user follow-up in deeper research contexts.
Fix: Allow optional footnote-style citations for power users who want sources expanded at the end.
🔁 Summary Table: Critical Issues & Fixes
Would you like me to refactor this prompt into a version that’s more user-proof, robust to ambiguity, and scalable for different depth levels?