One-liner: Synthesize outputs from three separate AI queries into a single coherent analysis — building the skill of triangulating AI perspectives.
Pick a question or topic you need to actually understand — a market trend, a technology choice, a strategic decision, a complex issue in your field.
Run three separate queries in three different AI sessions (or clear context between each). Each query approaches the same topic from a different angle:
Query 1 — The Optimist:
Analyze [your topic] from the most optimistic perspective. What's the strongest case that this will succeed/matter/grow? Cite specific evidence, trends, and examples. Be persuasive, not balanced.
Query 2 — The Skeptic:
Analyze [your topic] from a skeptical perspective. What's the strongest case that this is overhyped, risky, or likely to fail? Cite specific evidence, counterexamples, and historical parallels where similar things didn't pan out. Be rigorous, not cynical.
Query 3 — The Analyst:
Analyze [your topic] by identifying the 3-5 key variables that will determine the outcome. Don't argue for or against — map the decision space. For each variable, describe what would need to be true for a positive outcome vs. a negative one. Include what we don't yet know.
Now synthesize. Open a fresh document (not an AI chat). Write a 250-word brief that answers:
The brief should be something you'd share with a colleague or decision-maker. No AI jargon, no meta-commentary about the process.
Here's what you're about to do:
"Done" looks like: A 250-word brief you'd be comfortable sharing with a colleague, built from three distinct AI perspectives, with a clear statement of what you believe and what would change your mind.
In IS-Basic-01, you extracted insights from a single AI output. Here, you're building a fundamentally harder skill: triangulating across multiple AI perspectives to form your own judgment. This is exactly what senior decision-makers do with human advisors — they don't take any single perspective at face value. The discipline of writing the synthesis yourself (rather than asking AI to do it) ensures you're developing the judgment, not outsourcing it. This skill directly applies to research, due diligence, competitive analysis, and any situation where multiple data sources tell different stories.
Ready for more? Try IS-Advanced-01 — where you'll build a full research synthesis pipeline with structured evidence evaluation.
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